Publications

Ruixuan Sun, Ruoyan Kong, Qiao Jin, and Joseph Konstan. 2023. Less Can Be More: Exploring Population Rating Dispositions with Partitioned Models in Recommender Systems. In Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’23 Adjunct), 291–295. https://doi.org/10.1145/3563359.3597390

abstract:

In this study, we partition users by rating disposition - looking first at their percentage of negative ratings, and then at the general use of the rating scale. We hypothesize that users with different rating dispositions may use the recommender system differently and therefore the agreement with their past ratings may be less predictive of the future agreement. We use data from a large movie rating website to explore whether users should be grouped by disposition, focusing on identifying their various rating distributions that may hurt recommender effectiveness. We find that such partitioning not only improves computational efficiency but also improves top-k performance and predictive accuracy. Though such effects are largest for the user-based KNN CF, smaller for item-based KNN CF, and smallest for latent factor algorithms such as SVD.

links and other notes:

• https://doi.org/10.1145/3563359.3597390
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/JVIN79VK

Jasmine Jones, Ye Yuan, and Svetlana Yarosh. 2022. Be Consistent, Work the Program, Be Present Every Day: Exploring Technologies for Self-Tracking in Early Recovery. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 4: 164:1-164:26. https://doi.org/10.1145/3494955

abstract:

Recovery from substance abuse disorders (SUDs) is a lifelong process of change. Self-tracking technologies have been proposed by the recovery community as a beneficial design space to support people adopting positive lifestyles and behaviors in their recovery. To explore the potential of this design space, we designed and deployed a technology probe consisting of a mobile app, wearable visualization, and ambient display to enable people to track and reflect on the activities they adopted in their recovery process. With this probe we conducted a four-week exploratory field study with 17 adults in early recovery to investigate 1) what activities people in recovery desire to track, 2) how people perceive self-tracking tools in relation to their recovery process, and 3) what digital resources self-tracking tools can provide to aid the recovery process. Our findings illustrate the array of activities that people track in their recovery, along with usage scenarios, preferences and design tensions that arose. We discuss implications for holistic self-tracking technologies and opportunities for future work in behavior change support for this context.

links and other notes:

• https://doi.org/10.1145/3494955
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/E3RH7D3Q

Mo Houtti, Moyan Zhou, Loren Terveen, and Stevie Chancellor. 2022. “All of the White People Went First”: How Video Conferencing Consolidates Control and Exacerbates Workplace Bias. https://doi.org/10.48550/arXiv.2212.00849

abstract:

Workplace bias creates negative psychological outcomes for employees, permeating the larger organization. Workplace meetings are frequent, making them a key context where bias may occur. Video conferencing (VC) is an increasingly common medium for workplace meetings; we therefore investigated how VC tools contribute to increasing or reducing bias in meetings. Through a semi-structured interview study with 22 professionals, we found that VC features push meeting leaders to exercise control over various meeting parameters, giving leaders an outsized role in affecting bias. We demonstrate this with respect to four core VC features -- user tiles, raise hand, text-based chat, and meeting recording -- and recommend employing at least one of two mechanisms for mitigating bias in VC meetings -- 1) transferring control from meeting leaders to technical systems or other attendees and 2) helping meeting leaders better exercise the control they do wield.

links and other notes:

• arXiv:2212.00849 [cs]
• http://arxiv.org/abs/2212.00849
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ING2VF2M

Guy Aridor, Duarte Goncalves, Daniel Kluver, Ruoyan Kong, and Joseph Konstan. 2022. The Economics of Recommender Systems: Evidence from a Field Experiment on MovieLens. https://doi.org/10.48550/arXiv.2211.14219

abstract:

We conduct a field experiment on a movie-recommendation platform to identify if and how recommendations affect consumption. We use within-consumer randomization at the good level and elicit beliefs about unconsumed goods to disentangle exposure from informational effects. We find recommendations increase consumption beyond its role in exposing goods to consumers. We provide support for an informational mechanism: recommendations affect consumers' beliefs, which in turn explain consumption. Recommendations reduce uncertainty about goods consumers are most uncertain about and induce information acquisition. Our results highlight the importance of recommender systems' informational role when considering policies targeting these systems in online marketplaces.

links and other notes:

• arXiv:2211.14219 [cs, econ, q-fin]
• http://arxiv.org/abs/2211.14219
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/M9IAUJSI

Mo Houtti, Isaac Johnson, Joel Cepeda, Soumya Khandelwal, Aviral Bhatnagar, and Loren Terveen. 2022. “We Need a Woman in Music”: Exploring Wikipedia’s Values on Article Priority. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2: 266:1-266:28. https://doi.org/10.1145/3555156

abstract:

Wikipedia---like most peer production communities---suffers from a basic problem: the amount of work that needs to be done (articles to be created and improved) exceeds the available resources (editor effort). Recommender systems have been deployed to address this problem, but they have tended to recommend work tasks that match individuals' personal interests, ignoring more global community values. In English Wikipedia, discussion about Vital articles constitutes a proxy for community values about the types of articles that are most important, and should therefore be prioritized for improvement. We first analyzed these discussions, finding that an article's priority is considered a function of 1) its inherent importance and 2) its effects on Wikipedia's global composition. One important example of the second consideration is balance, including along the dimensions of gender and geography. We then conducted a quantitative analysis evaluating how four different article prioritization methods---two from prior research---would affect Wikipedia's overall balance on these two dimensions; we found significant differences among the methods. We discuss the implications of our results, including particularly how they can guide the design of recommender systems that take into account community values, not just individuals' interests.

links and other notes:

• https://doi.org/10.1145/3555156
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3WVE6D38

Ruoyan Kong, Charles Chuankai Zhang, Ruixuan Sun, Vishnu Chhabra, Tanushsrisai Nadimpalli, and Joseph A. Konstan. 2022. Multi-Objective Personalization in Multi-Stakeholder Organizational Bulk E-mail: A Field Experiment. Proceedings of the ACM on Human-Computer Interaction 6, CSCW2: 528:1-528:27. https://doi.org/10.1145/3555641

abstract:

Bulk email is often used in organizations to communicate "important-to-organization'' messages such as policy changes, organizational plans, and administrative updates. However, normal employees may prefer messages more relevant to their jobs or interests. Organizations face the challenge of balancing prioritizing the messages they prefer employees to know (tactical goals) while maintaining employees' positive experiences with these bulk emails, then they continue to read these emails in the future (strategic goals). Could personalization help organizations achieve these tactical and strategic goals? In an 8-week field experiment with a university newsletter, we implemented a 4x5x5 factorial design on personalizing subject lines, top news, and message order based on both the employees' and the organization's preferences. We measured these designs' influences on the open/interest/recognition/read-in-detail rate of the whole newsletter and the single messages within it. We found that ''important-to-organization'' messages only got higher recognition rates when being put on subject lines / top news (tactical goal). Mixing them with employee-preferred messages in top news did not bring further improvement to their own recognition rates but could improve the whole newsletter's recognition rate. Only when the top news solely contained the employee-preferred messages were the employees slightly more interested in the newsletter (strategic goal). We further analyze on which topics the employees and the organization's preferences conflicted. Finally, we discuss the design suggestions for organizational bulk email.

links and other notes:

• https://doi.org/10.1145/3555641
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RF4RNEMJ

Logan Stapleton, Devansh Saxena, Anna Kawakami, Tonya Nguyen, Asbjørn Ammitzbøll Flügge, Motahhare Eslami, Naja Holten Møller, Min Kyung Lee, Shion Guha, Kenneth Holstein, and Haiyi Zhu. 2022. Who Has an Interest in “Public Interest Technology”?: Critical Questions for Working with Local Governments & Impacted Communities. In Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing (CSCW’22 Companion), 282–286. https://doi.org/10.1145/3500868.3560484

abstract:

Local governments use a wide array of software, algorithms, and data systems across domains such as policing, probation, child protective services, courts, education, public employment services, homelessness services, etc. A growing body of work in CSCW and HCI has emerged to study, design, or demonstrate the boundaries of these technologies, oftentimes working with local governments. Local governments ostensibly aim to serve the public. So, some prior work has collaborated with local governments in the name of the public interest. However, others argue that local governments primarily police poor, minoritized communities, especially with increasingly limited funding for public services such as education or housing. These tensions raise critical questions: (How) should researchers collaborate with local governments? When should we oppose governments? How do we ethically engage with communities without being extractive? In this one-day workshop, we will bring together researchers from academia, the public sector, and community organizations to first take stock of work around public interest technologies. We will reflect on critical questions to orient the future of public interest technology and how we can work with, around, or against local governments while centering impacted communities.

links and other notes:

• https://doi.org/10.1145/3500868.3560484
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/WWDN2K7Q

Ashlee Milton and Stevie Chancellor. 2022. The Users Aren’t Alright: Dangerous Mental Illness Behaviors and Recommendations. Retrieved December 19, 2022 from http://arxiv.org/abs/2209.03941

abstract:

In this paper, we argue that recommendation systems are in a unique position to propagate dangerous and cruel behaviors to people with mental illnesses.

links and other notes:

• arXiv:2209.03941 [cs]
• http://arxiv.org/abs/2209.03941
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/95Z5LVRZ

Logan Stapleton, Min Hun Lee, Diana Qing, Marya Wright, Alexandra Chouldechova, Ken Holstein, Zhiwei Steven Wu, and Haiyi Zhu. 2022. Imagining new futures beyond predictive systems in child welfare: A qualitative study with impacted stakeholders. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22), 1162–1177. https://doi.org/10.1145/3531146.3533177

abstract:

Child welfare agencies across the United States are turning to data-driven predictive technologies (commonly called predictive analytics) which use government administrative data to assist workers’ decision-making. While some prior work has explored impacted stakeholders’ concerns with current uses of data-driven predictive risk models (PRMs), less work has asked stakeholders whether such tools ought to be used in the first place. In this work, we conducted a set of seven design workshops with 35 stakeholders who have been impacted by the child welfare system or who work in it to understand their beliefs and concerns around PRMs, and to engage them in imagining new uses of data and technologies in the child welfare system. We found that participants worried current PRMs perpetuate or exacerbate existing problems in child welfare. Participants suggested new ways to use data and data-driven tools to better support impacted communities and suggested paths to mitigate possible harms of these tools. Participants also suggested low-tech or no-tech alternatives to PRMs to address problems in child welfare. Our study sheds light on how researchers and designers can work in solidarity with impacted communities, possibly to circumvent or oppose child welfare agencies.

links and other notes:

• https://doi.org/10.1145/3531146.3533177
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/Z9A4G2ME

Mengchen Su, Lukas A. Olson, Daniel C. Jarratt, Sashank Varma, Joseph A. Konstan, Rebecca J. L. Keller, and Bodong Chen. 2022. Re-envisioning a K-12 Early Warning System with School Climate Factors. In Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S ’22), 405–408. https://doi.org/10.1145/3491140.3528670

abstract:

The Every Student Succeeds Act (ESSA) prescribes holistic measures of schools for student success and well-being. However, many early warning systems rely exclusively on the "Attendance, Behavior, Course" (ABC) taxonomy, which misses potentially crucial determinants such as school climate and students' socioemotional learning. We report early findings from a larger project that aims to apply machine learning methods to improve an early warning system by incorporating factors related to school climate and socioemotional learning. These preliminary analyses suggest that a culturally inclusive, socially supportive, and emotionally and physically safe school climate is related to academic success and fewer engagement/behavior problems at the school level. They suggest the promise of integrating these features into early warning systems to help schools change their practices to better support student well-being.

links and other notes:

• https://doi.org/10.1145/3491140.3528670
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UMG498D9

Hanlin Li, Brent Hecht, and Stevie Chancellor. 2022. Measuring the Monetary Value of Online Volunteer Work. Proceedings of the International AAAI Conference on Web and Social Media 16: 596–606. https://doi.org/10.1609/icwsm.v16i1.19318

abstract:

Online volunteers are a crucial labor force that keeps many for-profit systems afloat (e.g. social media platforms and online review sites). Despite their substantial role in upholding highly valuable technological systems, online volunteers have no way of knowing the value of their work. This paper uses content moderation as a case study and measures its monetary value to make apparent volunteer labor’s value. Using a novel dataset of private logs generated by moderators, we use linear mixed-effect regression and estimate that Reddit moderators worked a minimum of 466 hours per day in 2020. These hours are worth 3.4 million USD based on the median hourly wage for comparable content moderation services in the U.S. We discuss how this information may inform pathways to alleviate the one-sided relationship between technology companies and online volunteers.

links and other notes:

• https://ojs.aaai.org/index.php/ICWSM/article/view/19318
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3PHX2DSC

Hanlin Li, Brent Hecht, and Stevie Chancellor. 2022. All That’s Happening behind the Scenes: Putting the Spotlight on Volunteer Moderator Labor in Reddit. Proceedings of the International AAAI Conference on Web and Social Media 16: 584–595. https://doi.org/10.1609/icwsm.v16i1.19317

abstract:

Online volunteers are an uncompensated yet valuable labor force for many social platforms. For example, volunteer content moderators perform a vast amount of labor to maintain online communities. However, as social platforms like Reddit favor revenue generation and user engagement, moderators are under-supported to manage the expansion of online communities. To preserve these online communities, developers and researchers of social platforms must account for and support as much of this labor as possible. In this paper, we quantitatively characterize the publicly visible and invisible actions taken by moderators on Reddit, using a unique dataset of private moderator logs for 126 subreddits and over 900 moderators. Our analysis of this dataset reveals the heterogeneity of moderation work across both communities and moderators. Moreover, we find that analyzing only visible work – the dominant way that moderation work has been studied thus far – drastically underestimates the amount of human moderation labor on a subreddit. We discuss the implications of our results on content moderation research and social platforms.

links and other notes:

• https://ojs.aaai.org/index.php/ICWSM/article/view/19317
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/BWWSE2BA

Qiao Jin, Yu Liu, Svetlana Yarosh, Bo Han, and Feng Qian. 2022. How Will VR Enter University Classrooms? Multi-stakeholders Investigation of VR in Higher Education. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22), 1–17. https://doi.org/10.1145/3491102.3517542

abstract:

VR has received increased attention as an educational tool and many argue it is destined to influence educational practices, especially with the emergence of the Metaverse. Most prior research on educational VR reports on applications or systems designed for specified educational or training objectives. However, it is also crucial to understand current practices and attitudes across disciplines, having a holistic view to extend the body of knowledge in terms of VR adoption in an authentic setting. Taking a higher-level perception of people in different roles, we conducted a qualitative analysis based on 23 interviews with major stakeholders and a series of participatory design workshops with instructors and students. We identified the stakeholders who need to be considered for using VR in higher education, and highlighted the challenges and opportunities critical for VR current and potential practices in the university classroom. Finally, we discussed the design implications based on our findings. This study contributes a detailed description of current perceptions and considerations from a multi-stakeholder perspective, providing new empirical insights for designing novel VR and HCI technologies in higher education.

links and other notes:

• https://doi.org/10.1145/3491102.3517542
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/EQMJ7NKU

Hao-Fei Cheng, Logan Stapleton, Anna Kawakami, Venkatesh Sivaraman, Yanghuidi Cheng, Diana Qing, Adam Perer, Kenneth Holstein, Zhiwei Steven Wu, and Haiyi Zhu. 2022. How Child Welfare Workers Reduce Racial Disparities in Algorithmic Decisions. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI ’22), 1–22. https://doi.org/10.1145/3491102.3501831

abstract:

Machine learning tools have been deployed in various contexts to support human decision-making, in the hope that human-algorithm collaboration can improve decision quality. However, the question of whether such collaborations reduce or exacerbate biases in decision-making remains underexplored. In this work, we conducted a mixed-methods study, analyzing child welfare call screen workers’ decision-making over a span of four years, and interviewing them on how they incorporate algorithmic predictions into their decision-making process. Our data analysis shows that, compared to the algorithm alone, workers reduced the disparity in screen-in rate between Black and white children from 20% to 9%. Our qualitative data show that workers achieved this by making holistic risk assessments and adjusting for the algorithm’s limitations. Our analyses also show more nuanced results about how human-algorithm collaboration affects prediction accuracy, and how to measure these effects. These results shed light on potential mechanisms for improving human-algorithm collaboration in high-risk decision-making contexts.

links and other notes:

• https://doi.org/10.1145/3491102.3501831
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/XCEHP6MK

Jackie Chan, Aditi Atreyasa, Stevie Chancellor, and Eshwar Chandrasekharan. 2022. Community Resilience: Quantifying the Disruptive Effects of Sudden Spikes in Activity within Online Communities. In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems (CHI EA ’22), 1–8. https://doi.org/10.1145/3491101.3519813

abstract:

Online communities often experience some form of crisis. Regardless of whether these communities are small groups or entire platforms, the ability to handle disruptions during volatile periods signifies the resilience of a community. In this paper, we analyze the effects of a specific type of crisis event on Reddit: sudden spikes in attention received by a community due to a post from the subreddit hitting r/popular—the default feed that aggregates the most popular content on Reddit. Our results show that r/popular is a source of potential disruptions due to the higher number of comments, authors, and especially removed comments found compared to threads that do not reach r/popular. When looking at r/popular’s effects across subreddits, large communities’ commenting and posting behaviors were less impacted by r/popular threads compared to smaller communities. However, similar-sized subreddits had substantial variations in how their commenting and posting activities were influenced by an r/popular thread. Understanding the causal factors behind these differences has implications for online governance, as well as fostering resilient and healthy communities.

links and other notes:

• https://doi.org/10.1145/3491101.3519813
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/E47858WB

Charles Chuankai Zhang, Mo Houtti, C. Estelle Smith, Ruoyan Kong, and Loren Terveen. 2022. Working for the Invisible Machines or Pumping Information into an Empty Void? An Exploration of Wikidata Contributors’ Motivations. Proceedings of the ACM on Human-Computer Interaction 6, CSCW1: 135:1-135:21. https://doi.org/10.1145/3512982

abstract:

Structured data peer production (SDPP) platforms like Wikidata play an important role in knowledge production. Compared to traditional peer production platforms like Wikipedia, Wikidata data is more structured and intended to be used by machines, not (directly) by people; end-user interactions with Wikidata often happen through intermediary "invisible machines." Given this distinction, we wanted to understand Wikidata contributor motivations and how they are affected by usage invisibility caused by the machine intermediaries. Through an inductive thematic analysis of 15 interviews, we find that: (i) Wikidata editors take on two archetypes---Architects who define the ontological infrastructure of Wikidata, and Masons who build the database through data entry and editing; (ii) the structured nature of Wikidata reveals novel editor motivations, such as an innate drive for organizational work; (iii) most Wikidata editors have little understanding of how their contributions are used, which may demotivate some. We synthesize these insights to help guide the future design of SDPP platforms in supporting the engagement of different types of editors.

links and other notes:

• https://doi.org/10.1145/3512982
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TAG3PI49

Stevie Chancellor. 2022. Towards Practices for Human-Centered Machine Learning. https://doi.org/10.48550/arXiv.2203.00432

abstract:

"Human-centered machine learning" (HCML) is a term that describes machine learning that applies to human-focused problems. Although this idea is noteworthy and generates scholarly excitement, scholars and practitioners have struggled to clearly define and implement HCML in computer science. This article proposes practices for human-centered machine learning, an area where studying and designing for social, cultural, and ethical implications are just as important as technical advances in ML. These practices bridge between interdisciplinary perspectives of HCI, AI, and sociotechnical fields, as well as ongoing discourse on this new area. The five practices include ensuring HCML is the appropriate solution space for a problem; conceptualizing problem statements as position statements; moving beyond interaction models to define the human; legitimizing domain contributions; and anticipating sociotechnical failure. I conclude by suggesting how these practices might be implemented in research and practice.

links and other notes:

• arXiv:2203.00432 [cs]
• http://arxiv.org/abs/2203.00432
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CI6K9U6D

Svetlana Yarosh, Xizi Wang, and Yuan Yao. 2022. Perceptions of visual and multimodal symbolic mediated social touch: Role of technology modality, relationship, and task emotional salience. International Journal of Human-Computer Studies 159: 102757. https://doi.org/10.1016/j.ijhcs.2021.102757

abstract:

Mediated social touch technologies represent touch across distance as a mechanism for building and reinforcing social relationships. We investigated how people perceive and respond to mediated affective gestures given the relationship between participants (strangers vs. known partners), the task carried out (high vs. low emotional salience tasks), and the modality of mediated communication technology used (videochat vs. projector-camera duplexed workspace vs. projector-camera with shape-memory alloy haptics). Through a between-subjects 3 × 2 × 2 factorial design with 162 participants, we found that generally a projector-camera mediated social touch system without haptics performed no worse than one with haptics on most measures. However, we saw significant interaction effects, suggesting that haptics may offer some benefit for high-emotional salience tasks on measures of expressiveness and task load. We reflect on our findings by providing concrete recommendations on technologies to use, variables to include, and measures to consider for future mediated social touch research.

links and other notes:

• https://www.sciencedirect.com/science/article/pii/S1071581921001750
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/EZWZ9VQG

Amy R. Krentzman, Monica Sharratt, Kristin A. Mannella, Katherine T. Foster, Svetlana Yarosh, and Jennifer M. Jester. 2022. Women in sober living: baseline characteristics and their associations with reasons for leaving. Journal of Social Work Practice in the Addictions 22, 1: 21–39. https://doi.org/10.1080/1533256X.2021.1935156

abstract:

Studies of the intersectionality of gender, age, mental health, and physical health on sober living outcomes can inform service provision and improve results. Data were extracted from administrative records. Multinomial logistic regression was employed to determine the association between baseline characteristics and reasons for leaving. Women left sober living for three main reasons: relapsing (44%), breaking house rules (21%), and leaving on good terms (32%). Number of mental health conditions increased risk of relapse and rule-breaking. Women aged 18–29 compared to women 30+ had better physical and mental health but greater social instability. Women 18–29 were nearly three times more likely than women 30+ to be asked to leave for breaking rules. Women have diverse needs based on age and mental health status. Services and polices designed to address these needs can improve outcomes.

links and other notes:

• https://doi.org/10.1080/1533256X.2021.1935156
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UKAEJ6FT

Brent Hecht, Lauren Wilcox, Jeffrey P. Bigham, Johannes Schöning, Ehsan Hoque, Jason Ernst, Yonatan Bisk, Luigi De Russis, Lana Yarosh, Bushra Anjum, Danish Contractor, and Cathy Wu. 2021. It’s Time to Do Something: Mitigating the Negative Impacts of Computing Through a Change to the Peer Review Process. arXiv:2112.09544 [cs]. Retrieved February 10, 2022 from http://arxiv.org/abs/2112.09544

abstract:

The computing research community needs to work much harder to address the downsides of our innovations. Between the erosion of privacy, threats to democracy, and automation's effect on employment (among many other issues), we can no longer simply assume that our research will have a net positive impact on the world. While bending the arc of computing innovation towards societal benefit may at first seem intractable, we believe we can achieve substantial progress with a straightforward step: making a small change to the peer review process. As we explain below, we hypothesize that our recommended change will force computing researchers to more deeply consider the negative impacts of their work. We also expect that this change will incentivize research and policy that alleviates computing's negative impacts.

links and other notes:

• arXiv: 2112.09544
• http://arxiv.org/abs/2112.09544
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/B78YWFJQ

Loren Terveen. 2021. Commentary: “Autonomous” agents? What should we worry about? What should we do? Human–Computer Interaction 0, 0: 1–3. https://doi.org/10.1080/07370024.2021.1977129

abstract:

links and other notes:

• https://doi.org/10.1080/07370024.2021.1977129
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CZQIJ5DT

Dietmar Jannach, Pearl Pu, Francesco Ricci, and Markus Zanker. 2021. Recommender Systems: Past, Present, Future. AI Magazine 42, 3: 3–6. https://doi.org/10.1609/aimag.v42i3.18139

abstract:

The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender sys-tem requires more than a clever general-purpose algorithm. It requires an in-depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human-computer interaction problem, where a computerized system supports users in information search or decision-making contexts. This special issue contains a selection of papers reflecting this multi-faceted nature of the problem and puts open research challenges in recommender systems to the fore-front. It features articles on the latest learning technology, reflects on the human-computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.

links and other notes:

• https://ojs.aaai.org/index.php/aimagazine/article/view/18139
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TNUZ6HFW

Alexandra Papoutsaki, Jina Huh-Yoo, Haley MacLeod, Lena Mamykina, Andrew D. Miller, Svetlana Yarosh, and Daniel A. Epstein. 2021. The Future of Research on Online Health Communities: Discussing Membership, Structure, and Support. In Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing. Association for Computing Machinery, New York, NY, USA, 352–355. Retrieved February 10, 2022 from https://doi.org/10.1145/3462204.3481721

abstract:

Online health communities (OHCs) are spaces where people gather for informational and emotional support around specific medical conditions and concerns. Although OHCs are an active and exciting research area that continuously attracts a wide range of approaches and methodologies, the focus has been mostly on a limited selection of OHCs or medical conditions. At the same time, there are novel challenges that OHCs face, including changes to the medical system resulting from the COVID-19 crisis, increased medical misinformation propagating online, and additional focus on personalized medical advice that is less attainable in traditional medical systems. This workshop will bring together researchers to discuss and produce generalizable lessons about membership, structure, and support in OHCs in the context of these novel changes, generating research agendas for future exploration and design of OHCs.

links and other notes:

• https://doi.org/10.1145/3462204.3481721
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/6G6Z7RQ2

Avleen Kaur, C. Estelle Smith, and Loren Terveen. 2021. Sway Together, Stay Together: Visualizing Spiritual Support Networks Through the SoulGarden Prototype. In Companion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing. Association for Computing Machinery, New York, NY, USA, 84–88. Retrieved February 10, 2022 from https://doi.org/10.1145/3462204.3481774

abstract:

Spiritual support is a fundamental yet understudied dimension of support exchange in Online Health Communities. Recent work suggested that visualization of spiritual support networks offers a promising design mechanism for helping patients and caregivers navigate life-threatening physical or mental illnesses. Therefore, through user-centered and community-situated inquiry, we designed and developed SoulGarden–an interface that helps users visualize their spiritual support network in order to reduce loneliness and increase a sense of community and belonging. Initial feedback from user tests suggests that our nature-based theme provides users with feelings of calm and comfort, and that seeing the faces and prayers of supporters adds a meaningful sensory component to online support exchange. Our evaluation also suggests three valuable design considerations for future support network visualizations.

links and other notes:

• https://doi.org/10.1145/3462204.3481774
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FX5AZCJ4

C. Estelle Smith, William Lane, Hannah Miller Hillberg, Daniel Kluver, Loren Terveen, and Svetlana Yarosh. 2021. Effective Strategies for Crowd-Powered Cognitive Reappraisal Systems: A Field Deployment of the Flip*Doubt Web Application for Mental Health. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2: 417:1-417:37. https://doi.org/10.1145/3479561

abstract:

Online technologies offer great promise to expand models of delivery for therapeutic interventions to help users cope with increasingly common mental illnesses like anxiety and depression. For example, "cognitive reappraisal" is a skill that involves changing one's perspective on negative thoughts in order to improve one's emotional state. In this work, we present Flip*Doubt, a novel crowd-powered web application that provides users with cognitive reappraisals ("reframes") of negative thoughts. A one-month field deployment of Flip*Doubt with 13 graduate students yielded a data set of negative thoughts paired with positive reframes, as well as rich interview data about how participants interacted with the system. Through this deployment, our work contributes: (1) an in-depth qualitative understanding of how participants used a crowd-powered cognitive reappraisal system in the wild; and (2) detailed codebooks that capture informative context about negative input thoughts and reframes. Our results surface data-derived hypotheses that may help to explain what types of reframes are helpful for users, while also providing guidance to future researchers and developers interested in building collaborative systems for mental health. In our discussion, we outline implications for systems research to leverage peer training and support, as well as opportunities to integrate AI/ML-based algorithms to support the cognitive reappraisal task. (Note: This paper includes potentially triggering mentions of mental health issues and suicide.)

links and other notes:

• https://doi.org/10.1145/3479561
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/C5MAJRVT

Charles Chuankai Zhang and Loren Terveen. 2021. Quantifying the Gap: A Case Study of Wikidata Gender Disparities. In 17th International Symposium on Open Collaboration. Association for Computing Machinery, New York, NY, USA, 1–12. Retrieved February 10, 2022 from https://doi.org/10.1145/3479986.3479992

abstract:

Much prior research has found gender bias in peer production systems like Wikipedia and OpenStreetMap. This bias affects both women’s participation in these platforms and content about women on these platforms. We investigated the gender content gap in Wikidata, where less than 22% of items that represent people are about women. We asked: what is the source of this bias? Specifically, does it originate from the actions of Wikidata editors or from external factors; that is, does it simply reflect existing real world gender bias? We conducted a quantitative case study that found: (i) the most popular categories of people included in Wikidata represent male-dominant professions, such as American football; (ii) within a selected set of professions where we could obtain gender distribution data, Wikidata is no more biased than the real world: men and women are included at similar percentages, and the quality of items representing men and women also is similar. We provide possible explanations for our findings and implications for addressing the Wikidata content gap.

links and other notes:

• https://doi.org/10.1145/3479986.3479992
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TIZMSDQP

Hao-Fei Cheng, Logan Stapleton, Ruiqi Wang, Paige Bullock, Alexandra Chouldechova, Zhiwei Steven Steven Wu, and Haiyi Zhu. 2021. Soliciting Stakeholders’ Fairness Notions in Child Maltreatment Predictive Systems. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21), 1–17. https://doi.org/10.1145/3411764.3445308

abstract:

Recent work in fair machine learning has proposed dozens of technical definitions of algorithmic fairness and methods for enforcing these definitions. However, we still lack an understanding of how to develop machine learning systems with fairness criteria that reflect relevant stakeholders’ nuanced viewpoints in real-world contexts. To address this gap, we propose a framework for eliciting stakeholders’ subjective fairness notions. Combining a user interface that allows stakeholders to examine the data and the algorithm’s predictions with an interview protocol to probe stakeholders’ thoughts while they are interacting with the interface, we can identify stakeholders’ fairness beliefs and principles. We conduct a user study to evaluate our framework in the setting of a child maltreatment predictive system. Our evaluations show that the framework allows stakeholders to comprehensively convey their fairness viewpoints. We also discuss how our results can inform the design of predictive systems.

links and other notes:

• https://doi.org/10.1145/3411764.3445308
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/9P56ASRG

Sabirat Rubya, Joseph Numainville, and Svetlana Yarosh. 2021. Comparing Generic and Community-Situated Crowdsourcing for Data Validation in the Context of Recovery from Substance Use Disorders. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21), 1–17. https://doi.org/10.1145/3411764.3445399

abstract:

Targeting the right group of workers for crowdsourcing often achieves better quality results. One unique example of targeted crowdsourcing is seeking community-situated workers whose familiarity with the background and the norms of a particular group can help produce better outcome or accuracy. These community-situated crowd workers can be recruited in different ways from generic online crowdsourcing platforms or from online recovery communities. We evaluate three different approaches to recruit generic and community-situated crowd in terms of the time and the cost of recruitment, and the accuracy of task completion. We consider the context of Alcoholics Anonymous (AA), the largest peer support group for recovering alcoholics, and the task of identifying and validating AA meeting information. We discuss the benefits and trade-offs of recruiting paid vs. unpaid community-situated workers and provide implications for future research in the recovery context and relevant domains of HCI, and for the design of crowdsourcing ICT systems.

links and other notes:

• https://doi.org/10.1145/3411764.3445399
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/8FTCP8H4

Ye Yuan, Jan Cao, Ruotong Wang, and Svetlana Yarosh. 2021. Tabletop Games in the Age of Remote Collaboration: Design Opportunities for a Socially Connected Game Experience. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21), 1–14. https://doi.org/10.1145/3411764.3445512

abstract:

Prior research has highlighted opportunities for technology to better support the tabletop game experience in offline and online settings, but little work has focused on the social aspect of tabletop gaming. We investigated the social and collaborative aspects of tabletop gaming in the unique context of “social distancing” during the 2020 COVID-19 pandemic to shed light on the experience of remote tabletop gaming. With a multi-method qualitative approach (including digital ethnography and in-depth interviews), we empirically studied how people appropriate existing technologies and adapt their offline practices to play tabletop games remotely. We identify three themes that describe people's game and social experience during remote play: creating a shared tabletop environment (shared space), enabling a collective understanding (shared information and awareness), and facilitating a communal temporal experience (shared time). We reflect on challenges and design opportunities for a better experience in the age of remote collaboration.

links and other notes:

• https://doi.org/10.1145/3411764.3445512
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/B72AK6VH

Haiwei Ma, Sunny Parawala, and Svetlana Yarosh. 2021. Detecting Expressive Writing in Online Health Communities by Modeling Aggregated Empirical Data. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1: 62:1-62:32. https://doi.org/10.1145/3449136

abstract:

Although expressive writing has proved to be beneficial on physical, mental, and social health of individuals, it has been restrained to lab-based experimental studies. In the real world, individuals may naturally engage with expressive writing when dealing with difficult times, especially when facing a tough health journey. Health blogging may serve as an easy-to-access method for self-therapy, if spontaneous expressive writing occurs. However, many posts may not be expressive enough to provide the therapeutic power. In this study, we build a Gaussian naive Bayes model to detect expressive writing in an online health community, CaringBridge. Because we lack full text data as training data, we use a method to learn model parameters from meta-analysis of the literature. The classifier reaches reasonable accuracy on the test set annotated by the authors. We also explore factors that may influence users' spontaneous expressive writing. We find gender, health condition, author type, and privacy settings can affect individuals' spontaneous expressive writing. Finally, we reflect on our methodology and results and provide design implications for online health communities.

links and other notes:

• https://doi.org/10.1145/3449136
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/5UJJVGDF

C. Estelle Smith, Avleen Kaur, Katie Z. Gach, Loren Terveen, Mary Jo Kreitzer, and Susan O’Conner-Von. 2021. What is Spiritual Support and How Might It Impact the Design of Online Communities? Proceedings of the ACM on Human-Computer Interaction 5, CSCW1: 43:1-43:42. https://doi.org/10.1145/3449117

abstract:

Spirituality is an understudied topic in social computing; however, for Online Health Community (OHC) users facing life-threatening illness, it is of fundamental importance. Through in-depth focus groups with OHC stakeholders in a US context, we derive a definition of "spiritual support" for use by designers and researchers who study online social support. We show that spiritual support is an integral dimension that underlies other social support types, and that if we ignore spirituality in design, we fail to mitigate problematic issues that arise in online spaces when users' spiritual values clash. Based on participants' ideations, we provide design implications for OHCs and other social media to better facilitate spiritual support through: (1) representing spiritual beliefs, (2) assistance with supportive communication, (3) support network visualization and mobilization, and (4) advance care planning and digital legacy.

links and other notes:

• https://doi.org/10.1145/3449117
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/H6IP5AIX

Ruoyan Kong, Haiyi Zhu, and Joseph A. Konstan. 2021. Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1: 80:1-80:23. https://doi.org/10.1145/3449154

abstract:

Bulk email is a primary communication channel within organizations, with all-company emails and regular newsletters serving as a mechanism for making employees aware of policies and events. Ineffective communication could result in wasted employee time and a lack of compliance or awareness. Previous studies on organizational emails focused mostly on recipients. However, organizational bulk email system is a multi-stakeholder problem including recipients, communicators, and the organization itself. We studied the effectiveness, practice, and assessments of the organizational bulk email system of a large university from multi-stakeholders' perspectives. We conducted a qualitative study with the university's communicators, recipients, and managers. We delved into the organizational bulk email's distributing mechanisms of the communicators, the reading behaviors of recipients, and the perspectives on emails' values of communicators, managers, and recipients. We found that the organizational bulk email system as a whole was strained, and communicators are caught in the middle of this multi-stakeholder problem. First, though the communicators had an interest in preserving the effectiveness of channels in reaching employees, they had high-level clients whose interests might outweigh judgment about whether a message deserves widespread circulation. Second, though communicators thought they were sending important information, recipients viewed most of the organizational bulk emails as not relevant to them. Third, this disagreement was amplified by the success metric used by communicators. They viewed their bulk emails as successful if they had a high open rate. But recipients often opened and then rapidly discarded emails without reading the details. Last, while the communicators in general understood the challenge, they had a limited set of targeting and feedback tools to support their task.

links and other notes:

• https://doi.org/10.1145/3449154
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/A7GUAQ2R

Himan Abdollahpouri, Edward C. Malthouse, Joseph A. Konstan, Bamshad Mobasher, and Jeremy Gilbert. 2021. Toward the Next Generation of News Recommender Systems. In Companion Proceedings of the Web Conference 2021. Association for Computing Machinery, New York, NY, USA, 402–406. Retrieved February 10, 2022 from https://doi.org/10.1145/3442442.3452327

abstract:

This paper proposes a vision and research agenda for the next generation of news recommender systems (RS), called the table d’hôte approach. A table d’hôte (translates as host’s table) meal is a sequence of courses that create a balanced and enjoyable dining experience for a guest. Likewise, we believe news RS should strive to create a similar experience for the users by satisfying the news-diet needs of a user. While extant news RS considers criteria such as diversity and serendipity, and RS bundles have been studied for other contexts such as tourism, table d’hôte goes further by ensuring the recommended articles satisfy a diverse set of user needs in the right proportions and in a specific order. In table d’hôte, available articles need to be stratified based on the different ways that news can create value for the reader, building from theories and empirical research in journalism and user engagement. Using theories and empirical research from communication on the uses and gratifications (U&G) consumers derive from media, we define two main strata in a table d’hôte news RS, each with its own substrata: 1) surveillance, which consists of information the user needs to know, and 2) serendipity, which are the articles offering unexpected surprises. The diversity of the articles according to the defined strata and the order of the articles within the list of recommendations are also two important aspects of the table d’hôte in order to give the users the most effective reading experience. We propose our vision, link it to the existing concepts in the RS literature, and identify challenges for future research.

links and other notes:

• https://doi.org/10.1145/3442442.3452327
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/752SSCHA

Zachary Levonian, Marco Dow, Drew Erikson, Sourojit Ghosh, Hannah Miller Hillberg, Saumik Narayanan, Loren Terveen, and Svetlana Yarosh. 2021. Patterns of Patient and Caregiver Mutual Support Connections in an Online Health Community. Proceedings of the ACM on Human-Computer Interaction 4, CSCW3: 275:1-275:46. https://doi.org/10.1145/3434184

abstract:

Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a user's role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community---CaringBridge.org---where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users' peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships---patterns of repeated interactions---are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems.

links and other notes:

• https://doi.org/10.1145/3434184
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/MHXGQUE4

Adel Al-Dawood, Serene Alhajhussein, and Svetlana Yarosh. 2021. Saudi Arabian Parents’ Perception of Online Marital Matchmaking Technologies. Proceedings of the ACM on Human-Computer Interaction 4, CSCW3: 211:1-211:32. https://doi.org/10.1145/3432910

abstract:

Finding a date or a spouse online is usually considered an individualistic endeavor in Western cultures. This presents a challenge for collectivist non-Western cultures such as Saudi Arabia where choosing a spouse is viewed as a union of two families with parents of both spouses being heavily involved. Our work aims to investigate how Saudi Arabian parents view the utilization of technology by their young adults to seek potential spouses online. We report our findings of interviews conducted with 16 Saudi Arabian parents (8 fathers, 6 mothers and 1 couple). We generate qualitative themes that provide insights about how parents wanted to preserve their values, integrate technology into the traditional process and protect their young adults from potential harms. These themes lead to implications for designing suitable marital matchmaking technologies in Saudi Arabia and opportunities for future work.

links and other notes:

• https://doi.org/10.1145/3432910
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/QX4UKWHB

Zachary Levonian, Marco Dow, Drew Erikson, Sourojit Ghosh, Hannah Miller Hillberg, Saumik Narayanan, Loren Terveen, and Svetlana Yarosh. 2021. Patterns of Patient and Caregiver Mutual Support Connections in an Online Health Community. Proceedings of the ACM on Human-Computer Interaction 4, CSCW3: 275:1-275:46. https://doi.org/10.1145/3434184

abstract:

Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a user's role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community---CaringBridge.org---where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users' peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships---patterns of repeated interactions---are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems.

links and other notes:

• https://doi.org/10.1145/3434184
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NDU2CFRW

Haiwei Ma and Svetlana Yarosh. 2021. A Review of Affective Computing Research Based on Function-Component-Representation Framework. IEEE Transactions on Affective Computing: 1–1. https://doi.org/10.1109/TAFFC.2021.3104512

abstract:

links and other notes:

• https://ieeexplore.ieee.org/document/9512296/
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3WKUDLD9

Jingwen Zhang, Loren Terveen, and Lucy E. Dunne. 2021. The Ensemble-Building Challenge for Fashion Recommendation: Investigation of In-Home Practices and Assessment of Garment Combinations. In Recommender Systems in Fashion and Retail (Lecture Notes in Electrical Engineering), 101–116. https://doi.org/10.1007/978-3-030-66103-8_6

abstract:

Fashion is a domain that poses new and interesting challenges for recommender systems. While most recommendation problems seek a single-point solution (e.g. a product the user will purchase), individual garments must function within a wardrobe system, and must ultimately be matched with other garments to build an outfit. The outfit-building challenge is poorly understood in academic literature and professional practice. Here, we present data from two sources: subjective self-reports from consumers about their outfit-building practices, and assessments (by expert and crowd-sourced assessors) of computer-generated outfit combinations pulled from a real-world wardrobe. Results illuminate the objectives and obstacles of consumers in the daily dressing decision, and support the complexity of building combinations from a large set of individual garments.

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/SW9Q4JPS

Loren Terveen. 2020. Upcoming SIGCHI elections: some information and actions to be taken. Interactions 27, 6: 86–87. https://doi.org/10.1145/3427383

abstract:

links and other notes:

• https://doi.org/10.1145/3427383
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TTDF5TPA

Adel Al-Dawood, Serene Alhajhussein, and Svetlana Yarosh. 2020. Saudi Arabian Parents’ Perception of Online Marital Matchmaking Technologies. arXiv:2010.12022 [cs]. Retrieved from http://arxiv.org/abs/2010.12022

abstract:

Finding a date or a spouse online is usually considered an individualistic endeavor in Western cultures. This presents a challenge for collectivist non-Western cultures such as Saudi Arabia where choosing a spouse is viewed as a union of two families with parents of both spouses being heavily involved. Our work aims to investigate how Saudi Arabian parents view the utilization of technology by their young adults to seek potential spouses online. We report our findings of interviews conducted with 16 Saudi Arabian parents (8 fathers, 6 mothers and 1 couple). We generate qualitative themes that provide insights about how parents wanted to preserve their values, integrate technology into the traditional process and protect their young adults from potential harms. These themes lead to implications for designing suitable marital matchmaking technologies in Saudi Arabia and opportunities for future work.

links and other notes:

• arXiv: 2010.12022
• http://arxiv.org/abs/2010.12022
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/5NKC4TS4

Ruyuan Wan, Zachary Levonian, and Svetlana Yarosh. 2020. How much is a “like” worth? Engagement and Retention in an Online Health Community. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing (CSCW ’20 Companion), 413–417. https://doi.org/10.1145/3406865.3418320

abstract:

Online health communities are designed to help their users acquire social support, but developing self-sustaining communities capable of providing that support requires long-term user retention. Therefore, understanding the factors early in a user's experience that predict their long-term retention is important. In this study, we explore the impact of short-term visitor engagement on long-term user retention. We study users of CaringBridge.org, an online health community for communicating about health journeys, using survival analysis methods to quantify the impact of engagement on retention. First, we explicitly compare the impact of non-text "likes'' to text comments, surprisingly finding that likes exceed comments in their impact on retention for some users. Second, we compare less active and more active users in their response to visitor engagement, finding that more active users are less affected by short-term visitor engagement. We discuss the implications of our findings for the design of OHCs and for future work on visitor engagement.

links and other notes:

• https://doi.org/10.1145/3406865.3418320
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/G4PZKMM5

Prateek Vachher, Zachary Levonian, Hao-Fei Cheng, and Svetlana Yarosh. 2020. Understanding Community-Level Conflicts Through Reddit r/place. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing (CSCW ’20 Companion), 401–405. https://doi.org/10.1145/3406865.3418311

abstract:

Conflicts between communities in social-networking sites can degrade quality of communication and discourage participation, so understanding conflict dynamics can aid community management. However, studying inter-community conflict is challenging due to the open-ended nature of communication between communities. We study r/place, a 3-day pseudo-experiment on Reddit that provides an opportunity to observe inter-community conflict in a zero-sum environment. We quantify conflicts on r/place, identifying users and communities involved. We find that conflicts on r/place involve multiple communities on both the winning and losing side, and that communities get involved in conflicts due to geographic proximity on the canvas and due to existing political or cultural conflicts. Examining conflict winners reveals that total number of users is more important than highly-active users. Our results have implications for mitigating negative inter-community conflict on social-networking sites.

links and other notes:

• https://doi.org/10.1145/3406865.3418311
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RNWK6SHG

Mark Butzer, Zachary Levonian, Yangyang Luo, Kathleen Watson, Ye Yuan, C. Estelle Smith, and Svetlana Yarosh. 2020. Grandtotem: Supporting International and Intergenerational Relationships. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing (CSCW ’20 Companion), 227–231. https://doi.org/10.1145/3406865.3418307

abstract:

The grandchild-grandparent relationship faces unique challenges among international students. We developed the Grandtotem, which functions as a focal point for family ritual, a photo frame, and a relationship media album. Research with international student grandchildren helped to identify three dimensions along which their relationships with grandparents must adapt: (1) technology, (2) physical barriers, and (3) changing roles. Initial design feedback suggests that the Grandtotem is a promising tool for strengthening the social fabric during a difficult time of cultural transition.

links and other notes:

• https://doi.org/10.1145/3406865.3418307
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/S7T2TTS5

Oren Sar Shalom, Dietmar Jannach, and Joseph A. Konstan. 2020. Second Workshop on the Impact of Recommender Systems at ACM RecSys ’20. In Fourteenth ACM Conference on Recommender Systems, 630–631. https://doi.org/10.1145/3383313.3411471

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3383313.3411471
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/452J2BBD

Zachary Levonian, Marco Dow, Drew Erikson, Sourojit Ghosh, Hannah Miller Hillberg, Saumik Narayanan, Loren Terveen, and Svetlana Yarosh. 2020. Patterns of Patient and Caregiver Mutual Support Connections in an Online Health Community. arXiv:2007.16172 [cs]. Retrieved from http://arxiv.org/abs/2007.16172

abstract:

Online health communities offer the promise of support benefits to users, in particular because these communities enable users to find peers with similar experiences. Building mutually supportive connections between peers is a key motivation for using online health communities. However, a user's role in a community may influence the formation of peer connections. In this work, we study patterns of peer connections between two structural health roles: patient and non-professional caregiver. We examine user behavior in an online health community where finding peers is not explicitly supported. This context lets us use social network analysis methods to explore the growth of such connections in the wild and identify users' peer communication preferences. We investigated how connections between peers were initiated, finding that initiations are more likely between two authors who have the same role and who are close within the broader communication network. Relationships are also more likely to form and be more interactive when authors have the same role. Our results have implications for the design of systems supporting peer communication, e.g. peer-to-peer recommendation systems.

links and other notes:

• arXiv: 2007.16172
• http://arxiv.org/abs/2007.16172
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/M7E77A64

Joseph T. Yun, Claire M. Segijn, Stewart Pearson, Edward C. Malthouse, Joseph A. Konstan, and Venkatesh Shankar. 2020. Challenges and Future Directions of Computational Advertising Measurement Systems. Journal of Advertising: 1–13. https://doi.org/10.1080/00913367.2020.1795757

abstract:

links and other notes:

• https://www.tandfonline.com/doi/full/10.1080/00913367.2020.1795757
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/QXBMIXVM

Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, Jodi Forlizzi, and Haiyi Zhu. 2020. Keeping Designers in the Loop: Communicating Inherent Algorithmic Trade-offs Across Multiple Objectives. In Proceedings of the 2020 ACM Designing Interactive Systems Conference, 1245–1257. https://doi.org/10.1145/3357236.3395528

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3357236.3395528
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/WDVNB9R7

Ruoyan Kong, Haiyi Zhu, and Joseph A. Konstan. 2020. Learning to Ignore: A Case Study of Organization-Wide Bulk Email Effectiveness. arXiv:2006.16508 [cs]. Retrieved from https://dl.acm.org/doi/10.1145/3449154

abstract:

Bulk email is a primary communication channel within organizations, with all-company messages and regular newsletters serving as a mechanism for making employees aware of policies, events, and other needed messages. Ineffective communication could result in substantial wasted employee time and lack of awareness or compliance. Previous studies on organizational emails focused mostly on recipients. However, organizational bulk email systems are a multi-stakeholder problem including recipients, communicators, and organization itself. Thus we study the effectiveness, experience, practice, and assessments of organizational bulk email systems from different stakeholders' perspectives. We conducted a quantitative study within a large organization to understand the extent to which the recipients retained the messages from the organizational bulk emails they received. We conducted a qualitative study with communicators and recipients within the organization. We delved into the bulk email distributing mechanisms of the communicators, the reading behaviors of recipients, and the bulk emails' values from communicators and recipients' points of view. We found that the recipients were not retaining most of the messages from the bulk emails though they opened a large number of them. The tools for designing and distributing organizational bulk email for communicators were very limited. The assessments on bulk emails' values and effectiveness were different between communicators and recipients. We discussed possible solutions that could improve organizational bulk emails' designing and distributing mechanisms.

links and other notes:

• arXiv: 2006.16508
• https://dl.acm.org/doi/10.1145/3449154
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/X6GD5RGP

Saba Kawas, Ye Yuan, Akeiylah DeWitt, Qiao Jin, Susanne Kirchner, Abigail Bilger, Ethan Grantham, Julie A Kientz, Andrea Tartaro, and Svetlana Yarosh. 2020. Another decade of IDC research: examining and reflecting on values and ethics. In Proceedings of the Interaction Design and Children Conference (IDC ’20), 205–215. https://doi.org/10.1145/3392063.3394436

abstract:

Examining the values inherent in papers published at IDC provides a lens to our research community and informs the path of future research needs and opportunities. We conducted a content analysis of the values expressed in all full IDC papers between 2011 and 2019 and a survey with the first authors of 20% of these papers. We examine the types of IDC research contributions, the qualities and behaviors the research seeks to support in children, the role of the child and other stakeholders in the design process, the theories that inform IDC research, and the criteria that guide the technical design choices. We discuss the research contributions and the core value trends over the past two decades of IDC full published papers. We also present the ethical considerations central to the surveyed authors' work. Based on our analysis, we discuss implications and opportunities for future contributions, such as explicit attention to inclusivity in research, encouraging multidisciplinary collaborations, and expanding the qualities our community aims to support in children. These qualities include: focusing on children's sense of autonomy, agency, and empowerment; and children's participation in research as active creators of technology.

links and other notes:

• https://doi.org/10.1145/3392063.3394436
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/JMNXI7C8

Zachary Levonian, Drew Richard Erikson, Wenqi Luo, Saumik Narayanan, Sabirat Rubya, Prateek Vachher, Loren Terveen, and Svetlana Yarosh. 2020. Bridging Qualitative and Quantitative Methods for User Modeling: Tracing Cancer Patient Behavior in an Online Health Community. Proceedings of the International AAAI Conference on Web and Social Media 14: 405–416. Retrieved from https://aaai.org/ojs/index.php/ICWSM/article/view/7310

abstract:

Researchers construct models of social media users to understand human behavior and deliver improved digital services. Such models use conceptual categories arranged in a taxonomy to classify unstructured user text data. In many contexts, useful taxonomies can be defined via the incorporation of qualitative findings, a mixed-methods approach that offers the ability to create qualitatively-informed user models. But operationalizing taxonomies from the themes described in qualitative work is non-trivial and has received little explicit focus. We propose a process and explore challenges bridging qualitative themes to user models, for both operationalization of themes to taxonomies and the use of these taxonomies in constructing classification models. For classification of new data, we compare common keyword-based approaches to machine learning models. We demonstrate our process through an example in the health domain, constructing two user models tracing cancer patient experience over time in an online health community. We identify patterns in the model outputs for describing the longitudinal experience of cancer patients and reflect on the use of this process in future research.

links and other notes:

• https://aaai.org/ojs/index.php/ICWSM/article/view/7310
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/SSTQDDPU

C. Estelle Smith, Zachary Levonian, Haiwei Ma, Robert Giaquinto, Gemma Lein-Mcdonough, Zixuan Li, Susan O’Conner-Von, and Svetlana Yarosh. 2020. “I Cannot Do All of This Alone”: Exploring Instrumental and Prayer Support in Online Health Communities. arXiv:2005.11884 [cs]. Retrieved from http://arxiv.org/abs/2005.11884

abstract:

Online Health Communities (OHCs) are known to provide substantial emotional and informational support to patients and family caregivers facing life-threatening diagnoses like cancer and other illnesses, injuries, or chronic conditions. Yet little work explores how OHCs facilitate other vital forms of social support, especially instrumental support. We partner with CaringBridge.org---a prominent OHC for journaling about health crises---to complete a two-phase study focused on instrumental support. Phase one involves a content analysis of 641 CaringBridge updates. Phase two is a survey of 991 CaringBridge users. Results show that patients and family caregivers diverge from their support networks in their preferences for specific instrumental support types. Furthermore, ``prayer support'' emerged as the most prominent support category across both phases. We discuss design implications to accommodate divergent preferences and to expand the instrumental support network. We also discuss the need for future work to empower family caregivers and to support spirituality.

links and other notes:

• arXiv: 2005.11884
• http://arxiv.org/abs/2005.11884
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/98RFH63P

Weiwen Leung, Zheng Zhang, Daviti Jibuti, Jinhao Zhao, Maximilian Klein, Casey Pierce, Lionel Robert, and Haiyi Zhu. 2020. Race, Gender and Beauty: The Effect of Information Provision on Online Hiring Biases. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20), 1–11. https://doi.org/10.1145/3313831.3376874

abstract:

We conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers, and preferences towards Asian workers, female workers and more attractive workers. We also show that certain UI designs, including provision of candidates' information at the individual level and reducing the number of choices, can significantly reduce discrimination. However, provision of candidate's information at the subgroup level can increase discrimination. The results have practical implications for designing better online freelance marketplaces.

links and other notes:

• https://doi.org/10.1145/3313831.3376874
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/HF5IZRWT

Jeremy Heyer, Zachary Schmitt, Lynn Dombrowski, and Svetlana Yarosh. 2020. Opportunities for Enhancing Access and Efficacy of Peer Sponsorship in Substance Use Disorder Recovery. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20), 1–14. https://doi.org/10.1145/3313831.3376241

abstract:

Substance use disorders (SUDs) are characterized by an inability to decrease a substance use (e.g., alcohol or opioids) despite negative repercussions. SUDs are clinically diagnosable, hazardous, and considered a public health issue. Sponsorship, a specialized type of peer mentorship, is vital in the recovery process and originates from 12-step fellowship programs such as Alcoholics Anonymous (AA) and Narcotics Anonymous (NA). To investigate sponsorship relationship practices and to identify design opportunities for digitally-mediated peer support, we conducted 27 in-depth interviews with members of AA and NA. We identified five key sponsorship relationship practices relevant for designing social computing tools to support sponsorship and recovery: 1) assessing dyadic compatibility, 2) managing sponsorship with or without technology, 3) establishing boundaries, 4) building a peer support network, and 5) managing anonymity. We identify social computing and digitally-mediated design opportunities and implications.

links and other notes:

• https://doi.org/10.1145/3313831.3376241
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/D3SIN4WU

C. Estelle Smith, Bowen Yu, Anjali Srivastava, Aaron Halfaker, Loren Terveen, and Haiyi Zhu. 2020. Keeping Community in the Loop: Understanding Wikipedia Stakeholder Values for Machine Learning-Based Systems. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20), 1–14. https://doi.org/10.1145/3313831.3376783

abstract:

On Wikipedia, sophisticated algorithmic tools are used to assess the quality of edits and take corrective actions. However, algorithms can fail to solve the problems they were designed for if they conflict with the values of communities who use them. In this study, we take a Value-Sensitive Algorithm Design approach to understanding a community-created and -maintained machine learning-based algorithm called the Objective Revision Evaluation System (ORES)---a quality prediction system used in numerous Wikipedia applications and contexts. Five major values converged across stakeholder groups that ORES (and its dependent applications) should: (1) reduce the effort of community maintenance, (2) maintain human judgement as the final authority, (3) support differing peoples' differing workflows, (4) encourage positive engagement with diverse editor groups, and (5) establish trustworthiness of people and algorithms within the community. We reveal tensions between these values and discuss implications for future research to improve algorithms like ORES.

links and other notes:

• https://doi.org/10.1145/3313831.3376783
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2N8TZA6Q

Ruotong Wang, F. Maxwell Harper, and Haiyi Zhu. 2020. Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3376813

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3313831.3376813
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/F825WMEA

C. Estelle Smith, Eduardo Nevarez, and Haiyi Zhu. 2020. Disseminating Research News in HCI: Perceived Hazards, How-To’s, and Opportunities for Innovation. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3313831.3376744

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3313831.3376744
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TW22DINR

Carole-Jean Wu, Robin Burke, Ed H. Chi, Joseph Konstan, Julian McAuley, Yves Raimond, and Hao Zhang. 2020. Developing a Recommendation Benchmark for MLPerf Training and Inference. arXiv:2003.07336 [cs, stat]. Retrieved June 5, 2020 from http://arxiv.org/abs/2003.07336

abstract:

Deep learning-based recommendation models are used pervasively and broadly, for example, to recommend movies, products, or other information most relevant to users, in order to enhance the user experience. Among various application domains which have received significant industry and academia research attention, such as image classification, object detection, language and speech translation, the performance of deep learning-based recommendation models is less well explored, even though recommendation tasks unarguably represent significant AI inference cycles at large-scale datacenter fleets. To advance the state of understanding and enable machine learning system development and optimization for the commerce domain, we aim to define an industry-relevant recommendation benchmark for the MLPerf Training andInference Suites. The paper synthesizes the desirable modeling strategies for personalized recommendation systems. We lay out desirable characteristics of recommendation model architectures and data sets. We then summarize the discussions and advice from the MLPerf Recommendation Advisory Board.

links and other notes:

• arXiv: 2003.07336
• http://arxiv.org/abs/2003.07336
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/93777ESA

Joseph T Yun, Claire Segijn, Stewart Pearson, Edward C Malthouse, Joseph A Konstan, and Venkatesh Shankar. 2020. Challenges and Future Directions of Computational Advertising Measurement Systems. https://doi.org/10.13140/RG.2.2.35723.31525

abstract:

links and other notes:

• http://rgdoi.net/10.13140/RG.2.2.35723.31525
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/IWA4EQGK

Haiwei Ma, Hao-Fei Cheng, Bowen Yu, and Haiyi Zhu. 2019. Effects of Anonymity, Ephemerality, and System Routing on Cost in Social Question Asking. Proceedings of the ACM on Human-Computer Interaction Volume 3, GROUP. Retrieved January 7, 2020 from https://dl.acm.org/doi/abs/10.1145/3361119

abstract:

Online platforms provide new channels for people in need to seek help from friends and strangers. However, individuals often encounter psychological barriers that deter them from asking for help. For example, people might have different concerns about asking for help, including acknowledging incompetence, bothering others, and accruing social debt. These perceived social costs limit the potential benefits of help solicitations. In this study, we attempt to investigate whether anonymity (posting a question anonymously), ephemerality (allowing questions to be visible for only a short period), and system routing (having the system handle the question routing) could reduce social costs in a typical online help-seeking behavior-question asking. We built a platform to support these three features and conducted a controlled within-subjects experiment to test their effects on the social costs of posting questions. Results suggest that the presence of anonymity, ephemerality, and system routing reduce social costs. Further, we find that employing anonymity and system routing features did not lower the quality and quantity of answers to the questions in our system.

links and other notes:

• https://dl.acm.org/doi/abs/10.1145/3361119
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/N2BG9SCS

Bowen Yu, Ye Yuan, Loren Terveen, Zhiwei Steven Wu, and Haiyi Zhu. 2019. Designing Interfaces to Help Stakeholders Comprehend, Navigate, and Manage Algorithmic Trade-Offs. arXiv:1910.03061 [cs]. Retrieved May 21, 2020 from http://arxiv.org/abs/1910.03061

abstract:

Artificial intelligence algorithms have been applied to a wide variety of tasks, including assisting human decision making in high-stake contexts. However, these algorithms are complex and have trade-offs, notably between prediction accuracy and fairness to population subgroups. This makes it hard for domain stakeholders to understand algorithms and deploy them in a way that respects their goals and values. We created an interactive interface to help stakeholders explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their values. We evaluated our interface on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts. The interface proved effective at communicating algorithm trade-offs and supporting human decision making and also affected participants' trust in algorithmically aided decision making. Our results have implications for the deployment of intelligent algorithms and suggest important directions for future work.

links and other notes:

• arXiv: 1910.03061
• http://arxiv.org/abs/1910.03061
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FE5PSS8P

Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, and Joseph A. Konstan. 2019. From preference into decision making: modeling user interactions in recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems, 29–33. https://doi.org/10.1145/3298689.3347065

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3298689.3347065
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/B4JRRETW

Masitah Ghazali, Murni Mahmud, and Eunice Sari. 2019. myHCI-UX: taking HCI in Malaysia to greater heights. Interactions 26, 4: 84–85. https://doi.org/10.1145/3340111

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3342541.3340111
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2KFPTNUE

Hao-Fei Cheng, Ruotong Wang, Zheng Zhang, Fiona O’Connel, Terrance Gray, F. Maxwell Harper, and Haiyi Zhu. 2019. Explaining Decision-Making Algorithms through UI: Strategies to Help Non-Expert Stakeholders. In 2019 CHI Conference on Human Factors in Computing Systems. Retrieved January 7, 2020 from https://dl.acm.org/doi/abs/10.1145/3290605.3300789

abstract:

Increasingly, algorithms are used to make important decisions across society. However, these algorithms are usually poorly understood, which can reduce transparency and evoke negative emotions. In this research, we seek to learn design principles for explanation interfaces that communicate how decision-making algorithms work, in order to help organizations explain their decisions to stakeholders, or to support users' "right to explanation". We conducted an online experiment where 199 participants used different explanation interfaces to understand an algorithm for making university admissions decisions. We measured users' objective and self-reported understanding of the algorithm. Our results show that both interactive explanations and "white-box" explanations (i.e. that show the inner workings of an algorithm) can improve users' comprehension. Although the interactive approach is more effective at improving comprehension, it comes with a trade-off of taking more time. Surprisingly, we also find that users' trust in algorithmic decisions is not affected by the explanation interface or their level of comprehension of the algorithm.

links and other notes:

• https://dl.acm.org/doi/abs/10.1145/3290605.3300789
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UKFHT7E8

Ye Yuan, Stryker Thompson, Kathleen Watson, Alice Chase, Ashwin Senthilkumar, A. J. Bernheim Brush, and Svetlana Yarosh. 2019. Speech interface reformulations and voice assistant personification preferences of children and parents. International Journal of Child-Computer Interaction. https://doi.org/10.1016/j.ijcci.2019.04.005

abstract:

The pervasive availability of voice assistants may support children in finding answers to informational queries by removing the literacy requirements of text search (e.g., typing, spelling). However, most such systems are not designed for the specific needs and preferences of children and may struggle with understanding the intent of their questions. In our investigation, we observed 87 children and 27 adults interacting with three Wizard-of-Oz speech interfaces to arrive at answers to questions that required reformulation. We found that many children and some adults required help to reach an effective question reformulation. We report the common types of reformulations (both effective and ineffective ones) and the role of age in these. We also compared three versions of speech interfaces with different approaches to referring to itself (personification) and to the participant (naming personalization). We found that generally children preferred personified interfaces, but naming personalization was only preferred by younger children. We provide implications for design of speech systems for families.

links and other notes:

• http://www.sciencedirect.com/science/article/pii/S2212868918300898
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/4W5HD6E3

Soud Nassir, Adel Al-Dawood, Elham Alghamdi, and Eman Alyami. 2019. “My guardian did not approve!”: stories from fieldwork in Saudi Arabia. Interactions 26, 3: 44–49. https://doi.org/10.1145/3318145

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3328418.3318145
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/WQ5MCLYR

Sabirat Rubya, Xizi Wang, and Svetlana Yarosh. 2019. HAIR: Towards Developing a Global Self-Updating Peer Support Group Meeting List Using Human-Aided Information Retrieval. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, 83–92. https://doi.org/10.1145/3295750.3298933

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3295750.3298933
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/P6WG56NP

Elizabeth J. Polter, Christopher W. Wheldon, B. R. Simon Rosser, Nidhi Kohli, Benjamin D. Capistrant, Aditya Kapoor, Badrinath Konety, Darryl Mitteldorf, Michael Ross, Kristine M. C. Talley, Loren Terveen, William West, and Morgan M. Wright. 2019. Health-related quality of life by human immunodeficiency virus status in a cross-sectional survey of gay and bisexual prostate cancer survivors. Psycho-Oncology 28, 12: 2351–2357. https://doi.org/10.1002/pon.5228

abstract:

Objective Prostate cancer is the most common invasive cancer in gay and bisexual men (GBM). Despite the unique sexual and urinary concerns of this group, studies of prostate cancer rehabilitation have primarily focused on heterosexual men. GBM also have high prevalence of human immunodeficiency virus (HIV), which may be associated with lower health-related quality of life (HRQOL). We examined the association between HIV status and HRQOL in a cohort of GBM with prostate cancer. Methods Data from the Restore study, a cross-sectional online survey of GBM treated for prostate cancer, were used to examine this association. The Expanded Prostate Cancer Index Composite (EPIC) assessed function, bother, and summary measures in four domains: urinary, sexual, bowel, and hormone. Overall physical and mental HRQOL was assessed using the Short-Form Health Survey (SF-12). Multivariate analysis of variance and linear regression were used to evaluate the association between HIV status and HRQOL scores after adjustment for demographic and sexual characteristics. Results Of 192 participants, 24 (12.4%) reported an HIV diagnosis. After adjustment for covariates, HIV-positive status was associated with lower scores on the EPIC urinary (mean difference [MD]: −13.0, 95% CI, −21.4 to −4.6), sexual (MD: −12.5, 95% CI, −21.9 to −3.2), and bowel (MD: −5.9, 95% CI, −11.7 to −0.2) domains. No significant associations were observed between HIV status and other outcomes. Conclusions HIV status may be associated with poorer urinary, sexual, and bowel HRQOL in GBM prostate cancer survivors.

links and other notes:

• https://onlinelibrary.wiley.com/doi/abs/10.1002/pon.5228
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/26W2CMWF

Baris Unver, Jasmine Jones, Alexander Thayer, and Svetlana Yarosh. 2019. Heterogeneous Device Arrangements Affect Both Partners’ Experiences in Collaborative Media Spaces. In Human-Computer Interaction. Recognition and Interaction Technologies, Masaaki Kurosu (ed.). Springer International Publishing, Cham, 76–98. https://doi.org/10.1007/978-3-030-22643-5_7

abstract:

links and other notes:

• DOI: 10.1007/978-3-030-22643-5_7
• http://link.springer.com/10.1007/978-3-030-22643-5_7
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/XPXVVKHM

Sarah McRoberts, Ye Yuan, Kathleen Watson, and Svetlana Yarosh. 2019. Behind the Scenes: Design, Collaboration, and Video Creation with Youth. In Proceedings of the 18th ACM International Conference on Interaction Design and Children (IDC ’19), 173–184. https://doi.org/10.1145/3311927.3323134

abstract:

Youth content creators have been under-researched on video platforms such as Snapchat, YouTube, Instagram, etc. In order to better understand the ways that technology could support youth as content creators we conducted a 30-week series of workshops with an intergenerational design team including pre-teen youth and undergraduate designers-in-training. Qualitative analysis of workshop videos and notes revealed underlying challenges in video creation related to the social dynamics of creating videos in groups. In line with previous work, we note that a primary motivation for using social media is to spend time with friends. We found that existing video technology reveals opportunities and challenges to support the collaboration and interaction that motivates youth creators. We offer illustrative designs of potential collaborations in video sharing technology and propose that some of the concerns of youth social media use could be addressed by incorporating more opportunities for social creation.

links and other notes:

• event-place: Boise, ID, USA
• http://doi.acm.org/10.1145/3311927.3323134
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/EWPF7L7U

Ye Yuan and Svetlana Yarosh. 2019. Beyond Tutoring: Opportunities for Intergenerational Mentorship at a Community Level. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI ’19), 449:1-449:14. https://doi.org/10.1145/3290605.3300679

abstract:

Community intergenerational mentorship offers an opportunity to address older adults' social isolation while providing valuable one-on-one or small group learning experiences for elementary school students. Current organizations that support this kind of engagement focus on in-person visits that place the burden of logistics and transportation on the older adult. However, as older adults become less independent while aging, coming to schools in person becomes more challenging. We present a qualitative analysis of current intergenerational mentorship practices to understand opportunities for technology to expand access to this experience. We highlight elements critical for building successful mentorship: the importance of relationship building between older adults and children during mentoring activities, the skills mentors acquired to carry out mentoring activities, and support needed from teachers and schools. We contribute a rich description of current intergenerational mentorship practices and provide insights for opportunities for novel HCI technologies in this context.

links and other notes:

• event-place: Glasgow, Scotland Uk
• http://doi.acm.org/10.1145/3290605.3300679
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/Q22M6ZIU

Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, and Leonid Libkin. 2019. Dagstuhl Manifestos, Vol. 7, Issue 1 ISSN 2193-2433.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/GESGBVKW

Helen Fu, Terrence Adam, Joseph Konstan, Julian Wolfson, Thomas Clancy, and Jean Wyman. 2019. Influence of Patient Characteristics and Psychological Needs on Diabetes Mobile App Usability in Adults With Type 1 or Type 2 Diabetes: Crossover Randomized Trial. JMIR Diabetes 4, 2. https://doi.org/10.2196/11462

abstract:

Background: More than 1100 diabetes mobile apps are available, but app usage by patients is low. App usability may be influenced by patient factors such as age, sex, and psychological needs. Objective: Guided by Self-Determination Theory, the purposes of this study were to (1) assess the effect of patient characteristics on app usability, and (2) determine whether patient characteristics and psychological needs (competence, autonomy, and connectivity)—important for motivation in diabetes care—are associated with app usability. Methods: Using a crossover randomized design, 92 adults with type 1 or 2 diabetes tested two Android apps (mySugr and OnTrack) for seven tasks including data entry, blood glucose (BG) reporting, and data sharing. We used multivariable linear regression models to examine associations between patient characteristics, psychological needs, user satisfaction, and user performance (task time, success, and accuracy). Results: Participants had a mean age of 54 (range 19-74) years, and were predominantly white (62%, 57/92), female (59%, 54/92), with type 2 diabetes (70%, 64/92), and had education beyond high school (67%, 61/92). Participants rated an overall user satisfaction score of 62 (SD 18), which is considered marginally acceptable. The satisfaction mean score for each app was 55 (SD 18) for mySugr and 68 (SD 15) for OnTrack. The mean task completion time for all seven tasks was 7 minutes, with a mean task success of 82% and an accuracy rate of 68%. Higher user satisfaction was observed for patients with less education (P=.04) and those reporting more competence (P=.02), autonomy (P=.006), or connectivity with a health care provider (P=.03). User performance was associated with age, sex, education, diabetes duration, and autonomy. Older patients required more time (95% CI 1.1-3.2) and had less successful task completion (95% CI 3.5-14.3%). Men needed more time (P=.01) and more technical support than women (P=.04). High school education or less was associated with lower task success (P=.003). Diabetes duration of ≥10 years was associated with lower task accuracy (P=.02). Patients who desired greater autonomy and were interested in learning their patterns of BG and carbohydrates had greater task success (P=.049). Conclusions: Diabetes app usability was associated with psychological needs that are important for motivation. To enhance patient motivation to use diabetes apps for self-management, clinicians should address competence, autonomy, and connectivity by teaching BG pattern recognition and lifestyle planning, customizing BG targets, and reviewing home-monitored data via email. App usability could be improved for older male users and those with less education and greater diabetes duration by tailoring app training and providing ongoing technical support.

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/6X55ZWYD

Hao-Fei Cheng, Bowen Yu, Siwei Fu, Jian Zhao, Brent Hecht, Joseph Konstan, Loren Terveen, Svetlana Yarosh, and Haiyi Zhu. 2019. Teaching UI Design at Global Scales: A Case Study of the Design of Collaborative Capstone Projects for MOOCs. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (L@S ’19), 11:1-11:11. https://doi.org/10.1145/3330430.3333635

abstract:

Group projects are an essential component of teaching user interface (UI) design. We identified six challenges in transferring traditional group projects into the context of Massive Open Online Courses: managing dropout, avoiding free-riding, appropriate scaffolding, cultural and time zone differences, and establishing common ground. We present a case study of the design of a group project for a UI Design MOOC, in which we implemented technical tools and social structures to cope with the above challenges. Based on survey analysis, interviews, and team chat data from the students over a six-month period, we found that our socio-technical design addressed many of the obstacles that MOOC learners encountered during remote collaboration. We translate our findings into design implications for better group learning experiences at scale.

links and other notes:

• event-place: Chicago, IL, USA
• http://doi.acm.org/10.1145/3330430.3333635
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/5BVLW5WS

Helena Mentis, Eunice Sari, Loren Terveen, Allison Druin, Philippe Palanque, Cliff Lampe, Regina Bernhaupt, Anirudha Joshi, Susan Fussell, Susan Dray, Dan Olsen, Aaron Quigley, and Julie R. Williamson. 2018. The new SIGCHI EC’s values and strategic initiatives. Interactions 26, 1: 84–85. https://doi.org/10.1145/3292013

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3301428.3292013
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RRUWFGNA

Zachary Schmitt and Svetlana Yarosh. 2018. Participatory Design of Technologies to Support Recovery from Substance Use Disorders. Retrieved May 14, 2020 from https://doi.org/10.1145/3274425

abstract:

Substance use disorders, such as alcoholism and drug addiction, are a widespread and hazardous public health issue. Technology designed for the needs and values of people in recovery may be able to supplement traditional treatment options, enhance long-term abstinence maintenance, and create new opportunities for social support. We conducted a series of participatory design workshops with women in recovery from substance use disorders to identify design opportunities for supportive technologies that align with the specific values, practices and traditions of the recovery community. Through a data-driven inductive qualitative analysis, we identify five major themes that may be addressed with technology: 1) supporting twelve-step traditions and practices, 2) management of restlessness and moments of crisis, 3) agency and control over privacy and personal safety, 4) tracking progress and maintaining motivation, and 5) constructing a new normal. We connect these themes to specific implications for design.

links and other notes:

• https://doi.org/10.1145/3274425
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/IU4K3PR7

Haiyi Zhu, Bowen Yu, Aaron Halfaker, and Loren Terveen. 2018. Value-Sensitive Algorithm Design: Method, Case Study, and Lessons. Proc. ACM Hum.-Comput. Interact. 2, CSCW: 194:1-194:23. https://doi.org/10.1145/3274463

abstract:

Most commonly used approaches to developing automated or artificially intelligent algorithmic systems are Big Data-driven and machine learning-based. However, these approaches can fail, for two notable reasons: (1) they may lack critical engagement with users and other stakeholders; (2) they rely largely on historical human judgments, which do not capture and incorporate human insights into how the world can be improved in the future. We propose and describe a novel method for the design of such algorithms, which we call Value Sensitive Algorithm Design. Value Sensitive Algorithm Design incorporates stakeholders' tacit knowledge and explicit feedback in the early stages of algorithm creation. This increases the chance to avoid biases in design choices or to compromise key stakeholder values. Generally, we believe that algorithms should be designed to balance multiple stakeholders' needs, motivations, and interests, and to help achieve important collective goals. We also describe a specific project "Designing Intelligent Socialization Algorithms for WikiProjects in Wikipedia" to illustrate our method. We intend this paper to contribute to the rich ongoing conversation concerning the use of algorithms in supporting critical decision-making in society.

links and other notes:

• http://doi.acm.org/10.1145/3274463
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/P48TYI38

Andrew Hall, Loren Terveen, and Aaron Halfaker. 2018. Bot Detection in Wikidata Using Behavioral and Other Informal Cues. Proc. ACM Hum.-Comput. Interact. 2, CSCW: 64:1-64:18. https://doi.org/10.1145/3274333

abstract:

Bots have been important to peer production's success. Wikipedia, OpenStreetMap, and Wikidata all have taken advantage of automation to perform work at a rate and scale exceeding that of human contributors. Understanding the ways in which humans and bots behave in these communities is an important topic, and one that relies on accurate bot recognition. Yet, in many cases, bot activities are not explicitly flagged and could be mistaken for human contributions. We develop a machine classifier to detect previously unidentified bots using implicit behavioral and other informal editing characteristics. We show that this method yields a high level of fitness under both formal evaluation (PR-AUC: 0.845, ROC-AUC: 0.985) and a qualitative analysis of "anonymous" contributor edit sessions. We also show that, in some cases, unflagged bot activities can significantly misrepresent human behavior in analyses. Our model has the potential to support future research and community patrolling activities.

links and other notes:

• http://doi.acm.org/10.1145/3274333
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NQD62DPF

Hannah Miller Hillberg, Zachary Levonian, Daniel Kluver, Loren Terveen, and Brent Hecht. 2018. What I See is What You Don’T Get: The Effects of (Not) Seeing Emoji Rendering Differences Across Platforms. Proc. ACM Hum.-Comput. Interact. 2, CSCW: 124:1-124:24. https://doi.org/10.1145/3274393

abstract:

Emoji are popular in digital communication, but they are rendered differently on different viewing platforms (e.g., iOS, Android). It is unknown how many people are aware that emoji have multiple renderings, or whether they would change their emoji-bearing messages if they could see how these messages render on recipients' devices. We developed software to expose the multi-rendering nature of emoji and explored whether this increased visibility would affect how people communicate with emoji. Through a survey of 710 Twitter users who recently posted an emoji-bearing tweet, we found that at least 25% of respondents were unaware that the emoji they posted could appear differently to their followers. Additionally, after being shown how one of their tweets rendered across platforms, 20% of respondents reported that they would have edited or not sent the tweet. These statistics reflect millions of potentially regretful tweets shared per day because people cannot see emoji rendering differences across platforms. Our results motivate the development of tools that increase the visibility of emoji rendering differences across platforms, and we contribute our cross-platform emoji rendering software to facilitate this effort.

links and other notes:

• http://doi.acm.org/10.1145/3274393
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/PWXTXZK5

Juan F. Maestre, Elizabeth V. Eikey, Mark Warner, Svetlana Yarosh, Jessica Pater, Maia Jacobs, Gabriela Marcu, and Patrick C. Shih. 2018. Conducting Research with Stigmatized Populations: Practices, Challenges, and Lessons Learned. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing, 385–392. https://doi.org/10.1145/3272973.3273003

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3272973.3273003
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/Z83WBGTP

Joshua Wissbroecker and F. Maxwell Harper. 2018. Early Lessons from a Voice-Only Interface for Finding Movies. In arXiv:1808.09900 [cs]. Retrieved October 23, 2018 from http://arxiv.org/abs/1808.09900

abstract:

The current generation of streaming media players often allow users to speak commands (e.g., users can change the TV channel by pressing a button and saying "ESPN"). However, these devices typically support a narrow range of control- and search-oriented commands, and do not support deeper recommendation or exploration queries. To study spoken natural language interactions with recommenders, we have built MovieLens TV, a movie recommender system with no input modalities except voice. In this poster, we describe MovieLens TV, with a focus on lessons learned building a prototype system around an off-the-shelf Amazon Echo.

links and other notes:

• http://arxiv.org/abs/1808.09900
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/MURZ5EWG

Nicola Ferro, Norbert Fuhr, Gregory Grefenstette, Joseph A. Konstan, Pablo Castells, Elizabeth M. Daly, Thierry Declerck, Michael D. Ekstrand, Werner Geyer, Julio Gonzalo, Tsvi Kuflik, Krister Lindn, Bernardo Magnini, Jian-Yun Nie, Raffaele Perego, Bracha Shapira, Ian Soboroff, Nava Tintarev, Karin Verspoor, Martijn C. Willemsen, and Justin Zobel. 2018. The Dagstuhl Perspectives Workshop on Performance Modeling and Prediction. SIGIR Forum 52, 1: 91–101. https://doi.org/10.1145/3274784.3274789

abstract:

This paper reports the findings of the Dagstuhl Perspectives Workshop 17442 on performance modeling and prediction in the domains of Information Retrieval, Natural language Processing and Recommender Systems. We present a framework for further research, which identifies five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performance.

links and other notes:

• http://doi.acm.org/10.1145/3274784.3274789
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/M99EQ2E6

Svetlana Yarosh, Stryker Thompson, Kathleen Watson, Alice Chase, Ashwin Senthilkumar, Ye Yuan, and A. J. Bernheim Brush. 2018. Children asking questions: speech interface reformulations and personification preferences. In Proceedings of the 17th ACM Conference on Interaction Design and Children, 300–312. https://doi.org/10.1145/3202185.3202207

abstract:

links and other notes:

• BEST PAPER AWARD
• https://dl.acm.org/doi/10.1145/3202185.3202207
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/GCX3GMHP

Siwei Fu, Jian Zhao, Hao Fei Cheng, Haiyi Zhu, and Jennifer Marlow. 2018. T-Cal: Understanding Team Conversational Data with Calendar-based Visualization. 500. https://doi.org/10.1145/3173574.3174074

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?id=3173574.3174074
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/B2RX6E65

Michael D. Ekstrand and Joseph A. Konstan. 2018. Recommender Systems Notation: Proposed Common Notation for Teaching and Research. arXiv:1902.01348 [cs]. https://doi.org/10.18122/cs_facpubs/177/boisestate

abstract:

As the field of recommender systems has developed, authors have used a myriad of notations for describing the mathematical workings of recommendation algorithms. These notations ap-pear in research papers, books, lecture notes, blog posts, and software documentation. The dis-ciplinary diversity of the field has not contributed to consistency in notation; scholars whose home base is in information retrieval have different habits and expectations than those in ma-chine learning or human-computer interaction. In the course of years of teaching and research on recommender systems, we have seen the val-ue in adopting a consistent notation across our work. This has been particularly highlighted in our development of the Recommender Systems MOOC on Coursera (Konstan et al. 2015), as we need to explain a wide variety of algorithms and our learners are not well-served by changing notation between algorithms. In this paper, we describe the notation we have adopted in our work, along with its justification and some discussion of considered alternatives. We present this in hope that it will be useful to others writing and teaching about recommender systems. This notation has served us well for some time now, in research, online education, and traditional classroom instruction. We feel it is ready for broad use.

links and other notes:

• arXiv: 1902.01348
• http://arxiv.org/abs/1902.01348
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ER3IDXQS

Michael D. Ekstrand and Joseph A. Konstan. 2018. Recommender Systems Notation: Proposed Common Notation for Teaching and Research. arXiv:1902.01348 [cs]. https://doi.org/10.18122/cs_facpubs/177/boisestate

abstract:

As the field of recommender systems has developed, authors have used a myriad of notations for describing the mathematical workings of recommendation algorithms. These notations ap-pear in research papers, books, lecture notes, blog posts, and software documentation. The dis-ciplinary diversity of the field has not contributed to consistency in notation; scholars whose home base is in information retrieval have different habits and expectations than those in ma-chine learning or human-computer interaction. In the course of years of teaching and research on recommender systems, we have seen the val-ue in adopting a consistent notation across our work. This has been particularly highlighted in our development of the Recommender Systems MOOC on Coursera (Konstan et al. 2015), as we need to explain a wide variety of algorithms and our learners are not well-served by changing notation between algorithms. In this paper, we describe the notation we have adopted in our work, along with its justification and some discussion of considered alternatives. We present this in hope that it will be useful to others writing and teaching about recommender systems. This notation has served us well for some time now, in research, online education, and traditional classroom instruction. We feel it is ready for broad use.

links and other notes:

• arXiv: 1902.01348
• http://arxiv.org/abs/1902.01348
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/JAZMQGQ8

Daniel M. Russell and Svetlana Yarosh. 2018. Can We Look to Science Fiction for Innovation in HCI? Interactions 25, 2: 36–40. https://doi.org/10.1145/3178552

abstract:

links and other notes:

• http://doi.acm.org/10.1145/3178552
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/VHVIX8W8

Jacob Thebault-Spieker, Brent Hecht, and Loren Terveen. 2018. Geographic Biases are “Born, not Made”: Exploring Contributors’ Spatiotemporal Behavior in OpenStreetMap. In Proceedings of the 2018 ACM Conference on Supporting Groupwork, 71–82. https://doi.org/10.1145/3148330.3148350

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3148330.3148350
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/E47G5Z8I

Vikas Kumar, Sabirat Rubya, Joseph A. Konstan, and Loren G. Terveen. 2018. Risk “attention” or “adventure”: A qualitative study of novelty and familiarity in music listening. In CEUR Workshop Proceedings, 15–23. Retrieved May 21, 2020 from https://experts.umn.edu/en/publications/risk-attention-or-adventure-a-qualitative-study-of-novelty-and-fa

abstract:

links and other notes:

• https://experts.umn.edu/en/publications/risk-attention-or-adventure-a-qualitative-study-of-novelty-and-fa
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/PKJVRJ54

Jennifer Filson Moses, Patrick C. Dwyer, Paul Fuglestad, John Kim, Alexander Maki, Mark Snyder, and Loren Terveen. 2018. Encouraging online engagement: The role of interdependent self-construal and social motives in fostering online participation. Personality and Individual Differences 133: 47–55. https://doi.org/10.1016/j.paid.2017.06.035

abstract:

links and other notes:

• https://linkinghub.elsevier.com/retrieve/pii/S0191886917304245
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/5ENK6DM5

Jacob Thebault-Spieker, Aaron Halfaker, Loren G. Terveen, and Brent Hecht. 2018. Distance and Attraction: Gravity Models for Geographic Content Production. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18), 148:1-148:13. https://doi.org/10.1145/3173574.3173722

abstract:

Volunteered Geographic Information (VGI), such as contributions to OpenStreetMap and geotagged Wikipedia articles, is often assumed to be produced locally. However, recent work has found that peer-produced VGI is frequently contributed by non-locals. We evaluate this approach across hundreds of content types from Wikipedia, OpenStreetMap, and eBird, and show that these models can describe more than 90% of "VGI flows" for some content types. Our findings advance geographic HCI theory, suggesting some spatial mechanisms underpinning VGI production. We also discuss design implications that can help (a) human and algorithmic consumers of VGI evaluate the perspectives it contains and (b) address geographic coverage variations in these platforms (e.g. via more effective volunteer recruitment strategies).

links and other notes:

• http://doi.acm.org/10.1145/3173574.3173722
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CWKLQWIA

Harmanpreet Kaur, Isaac Johnson, Hannah J. Miller, Loren G. Terveen, Cliff Lampe, Brent Hecht, and Walter S. Lasecki. 2018. Oh The Places You’Ll Share: An Affordances-Based Model of Social Media Posting Behaviors. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (CHI EA ’18), LBW534:1-LBW534:6. https://doi.org/10.1145/3170427.3188601

abstract:

With rising use of multiple social network sites (SNSs), people now have an increasing number of options for audience, media, and other SNS features at their disposal. In this paper, our goal is to build machine learning models that can predict people»s multi-SNS posting decisions, thus enabling technology that can personalize and augment current SNS use. We explore affordances--the perceived utilities of a SNS»s features-for creating these models. We build an affordance-based model using data collected from a survey about people»s multi-SNS posting behavior (n=674). Our model predicts posting decisions that are ~35% more accurate compared to a random baseline, and ~10% more accurate than predictions based on SNS popularity.

links and other notes:

• http://doi.acm.org/10.1145/3170427.3188601
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/9BSYA8FH

Yuan Yao and F. Maxwell Harper. 2018. Judging Similarity: A User-centric Study of Related Item Recommendations. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), 288–296. https://doi.org/10.1145/3240323.3240351

abstract:

Related item recommenders operate in the context of a particular item. For instance, a music system's page about the artist Radio-head might recommend other similar artists such as The Flaming Lips. Often central to these recommendations is the computation of similarity between pairs of items. Prior work has explored many algorithms and features that allow for the computation of similarity scores, but little work has evaluated these approaches from a user-centric perspective. In this work, we build and evaluate six similarity scoring algorithms that span a range of activity- and content-based approaches. We evaluate the performance of these algorithms using both offline metrics and a new set of more than 22,000 user-contributed evaluations. We integrate these results with a survey of more than 700 participants concerning their expectations about item similarity and related item recommendations. We find that content-based algorithms outperform ratings- and clickstream-based algorithms in terms of how well they match user expectations for similarity and recommendation quality. Our results yield a number of implications to guide the construction of related item recommendation algorithms.

links and other notes:

• published version: https://dl.acm.org/authorize?N661710
• dataset: https://doi.org/10.13020/D67700
• http://doi.acm.org/10.1145/3240323.3240351
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/GA4KHWED

Yifan Zhong, Tahir Lazaro Sousa Menezes, Vikas Kumar, Qian Zhao, and F. Maxwell Harper. 2018. A Field Study of Related Video Recommendations: Newest, Most Similar, or Most Relevant? In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), 274–278. https://doi.org/10.1145/3240323.3240395

abstract:

Many video sites recommend videos related to the one a user is watching. These recommendations have been shown to influence what users end up exploring and are an important part of a recommender system. Plenty of methods have been proposed to recommend related videos, but there has been relatively little work that compares competing strategies. We describe a field study of related video recommendations, where we deploy algorithms to recommend related movie trailers. Our results show that recency- and similarity-based algorithms yield the highest click-through rates, and that the recency-based algorithm leads to the most trailer-level engagement. Our findings suggest the potential to design non-personalized yet effective related item recommendation strategies.

links and other notes:

• published version: https://dl.acm.org/authorize?N661718
• best short paper nominee
• http://doi.acm.org/10.1145/3240323.3240395
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/5TVFN3UA

Daniel Kluver, Michael D. Ekstrand, and Joseph A. Konstan. 2018. Rating-Based Collaborative Filtering: Algorithms and Evaluation. In Social Information Access. Springer, Cham, 344–390. https://doi.org/10.1007/978-3-319-90092-6_10

abstract:

Recommender systems help users find information by recommending content that a user might not know about, but will hopefully like. Rating-based collaborative filtering recommender systems do this by finding patterns that are consistent across the ratings of other users. These patterns can be used on their own, or in conjunction with other forms of social information access to identify and recommend content that a user might like. This chapter reviews the concepts, algorithms, and means of evaluation that are at the core of collaborative filtering research and practice. While there are many recommendation algorithms, the ones we cover serve as the basis for much of past and present algorithm development. After presenting these algorithms we present examples of two more recent directions in recommendation algorithms: learning-to-rank and ensemble recommendation algorithms. We finish by describing how collaborative filtering algorithms can be evaluated, and listing available resources and datasets to support further experimentation. The goal of this chapter is to provide the basis of knowledge needed for readers to explore more advanced topics in recommendation.

links and other notes:

• DOI: 10.1007/978-3-319-90092-6_10
• https://link.springer.com/chapter/10.1007/978-3-319-90092-6_10
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/HWEX2ATB

Raghav Pavan Karumur, Bowen Yu, Haiyi Zhu, and Joseph A. Konstan. 2018. Content is King, Leadership Lags: Effects of Prior Experience on Newcomer Retention and Productivity in Online Production Groups. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18), 506:1-506:13. https://doi.org/10.1145/3173574.3174080

abstract:

Organizers of online groups often struggle to recruit members who can most effectively carry out the group's activities and remain part of the group over time. In a study of a sample of 30,000 new editors belonging to 1,054 English WikiProjects, we empirically examine the effects of generalized prior work-productivity experience (measured by overall prior article edits), prior leadership experience (measured by overall prior project edits), and localized prior work-productivity experience (measured by pre-joining article edits on a project) on early retention and productivity. We find that (1)generalized prior work-productivity experience is positively associated with retention, but negatively associated with productivity (2) prior leadership experience is negatively associated with both retention and productivity, and (3) localized prior work-productivity experience is positively associated with both retention and productivity within that focal project. We then discuss implications to inform the designs of early interventions aimed at group success.

links and other notes:

• http://doi.acm.org/10.1145/3173574.3174080
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2PACT7GB

Christine T. Wolf, Haiyi Zhu, Julia Bullard, Min Kyung Lee, and Jed R. Brubaker. 2018. The Changing Contours of “Participation” in Data-driven, Algorithmic Ecosystems: Challenges, Tactics, and an Agenda. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’18), 377–384. https://doi.org/10.1145/3272973.3273005

abstract:

Contributing to a growing attention to algorithms and algorithmic interaction in the CHI and CSCW communities, this workshop aims to deal centrally with the topic of human "participation" and its changing role to data-driven, algorithmic ecosystems. Such a focus includes projects that involve users in the design of algorithms and "human-in-the-loop" systems, broader investigations into the ways in which "participation" is situated in data-driven activities, as well as conceptual concerns about participation's changing contours in contemporary social computing landscapes. This one- day workshop will be led by academic and industry researchers and sets out to achieve three goals: identify cases and ongoing projects on the topic of participation in algorithmic ecosystems; create a tactical toolkit of key challenges and strategies in this space; and set a forward-facing agenda to provoke further attention to the changing role of participation in contemporary sociotechnical systems.

links and other notes:

• http://doi.acm.org/10.1145/3272973.3273005
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/WYHLGXQN

Juan F. Maestre, Elizabeth V. Eikey, Mark Warner, Svetlana Yarosh, Jessica Pater, Maia Jacobs, Gabriela Marcu, and Patrick C. Shih. 2018. Conducting Research with Stigmatized Populations: Practices, Challenges, and Lessons Learned. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’18), 385–392. https://doi.org/10.1145/3272973.3273003

abstract:

Conducting research with communities who are at risk of being stigmatized can be a challenging endeavor. It is often difficult to reach and recruit individuals for research purposes regarding a stigmatized condition or situation. Yet, researchers in our field have recognized the importance of work in this area and have individually developed a range of strategies to reach, recruit, and work with these populations. This workshop will invite researchers and practitioners to present, discuss, and compare strategies and experiences when working with stigmatized communities in the context of the ever-evolving nature of technology. The outcomes of the workshop will include an outline for an article that will summarize the strategies and practices discussed as well as identify the approaches that have led to the best outcomes across different populations.

links and other notes:

• http://doi.acm.org/10.1145/3272973.3273003
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/VG9EU4MU

Changye Li, Zachary Levonian, Haiwei Ma, and Svetlana Yarosh. 2018. Condition Unknown: Predicting Patients’ Health Conditions in an Online Health Community. In Companion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’18), 281–284. https://doi.org/10.1145/3272973.3274075

abstract:

Online health communities rely on information about their users to provide services to members. We partner with the online health community CaringBridge.org to infer the health condition that users are discussing from their early writing on the site. We utilize the self-reported health condition data that is provided by users to train machine learning classifiers to predict the health condition of non-reporting users. An analysis of the classifier's errors reveals that users frequently discuss multiple health conditions. We present models with explainable features, enabling us to extract words for the enrichment of consumer health vocabularies and to support future designs connecting patients.

links and other notes:

• http://doi.acm.org/10.1145/3272973.3274075
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/WECVDZIR

Taavi T. Taijala, Martijn C. Willemsen, and Joseph A. Konstan. 2018. Movieexplorer: Building an Interactive Exploration Tool from Ratings and Latent Taste Spaces. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC ’18), 1383–1392. https://doi.org/10.1145/3167132.3167281

abstract:

This 1 paper introduces and evaluates MovieExplorer, an interactive exploration tool designed to use the data available in a traditional ratings-based recommender system to provide an interactive interface more suited to user exploration and fulfillment of short-term recommendation needs. A field deployment with 1,950 users showed that users found the tool useful for a variety of exploration and short-term recommendation tasks, even preferring it to existing interfaces for several tasks. Experimentation with several design features found that the actual user navigation algorithms were significant (user satisfaction was lower for algorithms with faked controls) and that offering positive and negative feedback options led to increased feedback and user retention.

links and other notes:

• http://doi.acm.org/10.1145/3167132.3167281
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/PZ6N39MC

Andrew Hall, Jacob Thebault-Spieker, Shilad Sen, Brent Hecht, and Loren Terveen. 2018. Exploring the Relationship Between “Informal Standards” and Contributor Practice in OpenStreetMap. In Proceedings of the 14th International Symposium on Open Collaboration (OpenSym ’18), 10:1-10:11. https://doi.org/10.1145/3233391.3233962

abstract:

Peer production communities create valuable content such as software, encyclopedia articles, and map data. As part of the creation process, these communities define production standards for their content, e.g., semantic and syntactic requirements. We carried out a study in OpenStreetMap to investigate the role of that community's standards for geographic metadata. We found that most applied metadata was consistent with the community's standards; however, we also found that the standards identified many opportunities for applying metadata that were not achieved. In addition, when we situated the standards in the context of OpenStreetMap's data model, we found a significant amount of ambiguity; the syntax allowed only one value, but everyday meaning -- and the standards themselves -- called for multiple values. Our results suggest significant opportunities for OpenStreetMap to produce additional valuable open source content to power applications.

links and other notes:

• http://doi.acm.org/10.1145/3233391.3233962
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/F7VPAUFX

Qian Zhao, Martijn C. Willemsen, Gediminas Adomavicius, F. Maxwell Harper, and Joseph A. Konstan. 2018. Interpreting User Inaction in Recommender Systems. In Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18), 40–48. https://doi.org/10.1145/3240323.3240366

abstract:

Temporally, users browse and interact with items in recommender systems. However, for most systems, the majority of the displayed items do not elicit any action from users. In other words, the user-system interaction process includes three aspects: browsing, action, and inaction. Prior recommender systems literature has focused more on actions than on browsing or inaction. In this work, we deployed a field survey in a live movie recommender system to interpret what inaction means from both the user's and the system's perspective, guided by psychological theories of human decision making. We further systematically study factors to infer the reasons of user inaction and demonstrate with offline data sets that this descriptive and predictive inaction model can provide benefits for recommender systems in terms of both action prediction and recommendation timing.

links and other notes:

• http://doi.acm.org/10.1145/3240323.3240366
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UGI5T2EQ

Denis Kotkov, Joseph A. Konstan, Qian Zhao, and Jari Veijalainen. 2018. Investigating Serendipity in Recommender Systems Based on Real User Feedback. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC ’18), 1341–1350. https://doi.org/10.1145/3167132.3167276

abstract:

Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared those definitions of serendipity based on user responses. We found that most kinds of serendipity and all the variations of serendipity components broaden user preferences, but one variation of unexpectedness hurts user satisfaction. We found effective features for detecting serendipitous movies according to definitions that do not include this variation of unexpectedness. We also found that different variations of unexpectedness and different kinds of serendipity have different effects on preference broadening and user satisfaction. Among movies users rate in our system, up to 8.5% are serendipitous according to at least one definition of serendipity, while among recommendations that users receive and follow in our system, this ratio is up to 69%.

links and other notes:

• http://doi.acm.org/10.1145/3167132.3167276
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/Y6Y46ADB

Qian Zhao, F. Maxwell Harper, Gediminas Adomavicius, and Joseph A. Konstan. 2018. Explicit or Implicit Feedback? Engagement or Satisfaction?: A Field Experiment on Machine-learning-based Recommender Systems. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (SAC ’18), 1331–1340. https://doi.org/10.1145/3167132.3167275

abstract:

Recommender systems algorithms are generally evaluated primarily on machine learning criteria such as recommendation accuracy or top-n precision. In this work, we evaluate six recommendation algorithms from a user-centric perspective, collecting both objective user activity data and subjective user perceptions. In a field experiment involving 1508 users who participated for at least a month, we compare six algorithms built using machine learning techniques, ranging from supervised matrix factorization, contextual bandit learning to Q learning. We found that the objective design in machine-learning-based recommender systems significantly affects user experience. Specifically, a recommender optimizing for implicit action prediction error engages users more than optimizing for explicit rating prediction error when modeled with the classical matrix factorization algorithms, which empirically explains the historical transition of recommender system research from modeling explicit feedback data to implicit feedback data. However, the action-based recommender is not as precise as the rating-based recommender in that it increases not only positive engagement but also negative engagement, e.g., negative action rate and user browsing effort which are negatively correlated with user satisfaction. We show that blending both explicit and implicit feedback from users through an online learning algorithm can gain the benefits of engagement and mitigate one of the possible costs (i.e., the increased browsing effort).

links and other notes:

• http://doi.acm.org/10.1145/3167132.3167275
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/J9J9PVWC

C. Estelle Smith, Xinyi Wang, Raghav Pavan Karumur, and Haiyi Zhu. 2018. [Un]Breaking News: Design Opportunities for Enhancing Collaboration in Scientific Media Production. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18), 381:1-381:13. https://doi.org/10.1145/3173574.3173955

abstract:

Contemporary scientific media production requires a complex socio-technical infrastructure we call the "Media Production Pipeline" (MPP). Media professionals engage with researchers along the MPP to disseminate science news to the lay public. However, differing incentive structures and professional contexts frequently set researchers' values and needs at odds with those of media professionals, resulting in problematic or failed interactions. We ask the research question: what pain points in scientific media production afford opportunities for future HCI innovation? We then present a grounded theory analysis of 24 interviews with researchers and media professionals, yielding several key contributions. First, we describe two collaborative domains in scientific media production between research advocates and media outlets. Second, we characterize discrete technological gaps and pain points in both domains. Finally, we discuss implications for design and propose solutions from HCI areas like peer production, online communities, recommender systems, and online collaboration.

links and other notes:

• http://doi.acm.org/10.1145/3173574.3173955
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/DIDWEHKS

Jacob Thebault-Spieker, Brent Hecht, and Loren Terveen. 2018. Geographic Biases are “Born, not Made”: Exploring Contributors’ Spatiotemporal Behavior in OpenStreetMap. 71–82. https://doi.org/10.1145/3148330.3148350

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3148330.3148350
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/BZ5IAPLB

Sabirat Rubya and Svetlana Yarosh. 2017. Interpretations of Online Anonymity in Alcoholics Anonymous and Narcotics Anonymous. Retrieved May 14, 2020 from https://doi.org/10.1145/3134726

abstract:

How do individuals in twelve-step fellowships like Alcoholics Anonymous (AA) and Narcotics Anonymous (NA) interpret and enact "anonymity?" In this paper, we answer this question through a mixed-methods investigation. Through secondary analysis of interview data from 26 participants and an online questionnaire (N=285) we found three major interpretations of anonymity among AA and NA members: "unidentifiability," "social contract," and "program over individual." While unidentifiability has been the focus of computing investigations, the other interpretations provide a significant and novel lens on anonymity. To understand how and when the unidentifiability interpretation was most likely to be enacted, we conducted a quantitative analysis of traces of activity in a large online recovery community. We observed that members were less likely to enact "unidentifiability" if they were more connected to the particular community and had more time in recovery. We provide implications for future research on context-specific anonymity and implications for design in online recovery spaces and similar sensitive contexts.

links and other notes:

• https://doi.org/10.1145/3134726
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/DP75CQXN

Kenya Mejia and Svetlana Yarosh. 2017. A Nine-Item Questionnaire for Measuring the Social Disfordance of Mediated Social Touch Technologies. Retrieved May 14, 2020 from https://doi.org/10.1145/3134712

abstract:

Mediated Social Touch (MST) technologies focus on enhancing a communication experience by sensing, transmitting, and simulating social touch between remote partners. With interest in developing MST technologies continuing to grow, it is important to create standardized methods for measuring the effect of these novel systems. We designed and validated a 9-item questionnaire to measure the "Social Disfordance" of Mediated Social Touch, with three scales that focus on Social Discomfort, Communicational Expressiveness, and Need for Additional Consideration. A high degree of "social disfordance" of an MST system signifies that it may not provide the appropriate social affordances for mediating touch in a particular context. The development of the Social Disfordance of Mediated Social Touch (SDMST) instrument included a systematic literature review, expert feedback, and think-out-loud piloting. Its refinement included an exploratory factor analysis with a subsequent reduction of questions and scales. We report its psychometric properties, including metrics of inter-item reliability, convergent validity, test-retest reliability, and concurrent validity, confirming that these properties are sufficient for future use. We conclude with examples of scoring, appropriate use, and a discussion of the limitations.

links and other notes:

• https://doi.org/10.1145/3134712
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/PSSZDT54

Svetlana Yarosh, Kenya Mejia, Baris Unver, Xizi Wang, Yuan Yao, Akin Campbell, and Brad Holschuh. 2017. SqueezeBands: Mediated Social Touch Using Shape Memory Alloy Actuation. Proceedings of the ACM on Human-Computer Interaction 1, CSCW: 1–18. https://doi.org/10.1145/3134751

abstract:

links and other notes:

• https://dl.acm.org/doi/10.1145/3134751
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/4UICUBPQ

Haiwei Ma, C. Estelle Smith, Lu He, Saumik Narayanan, Robert A. Giaquinto, Roni Evans, Linda Hanson, and Svetlana Yarosh. 2017. Write for Life: Persisting in Online Health Communities through Expressive Writing and Social Support. Proceedings of the ACM on Human-Computer Interaction 1, CSCW: 1–24. https://doi.org/10.1145/3134708

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3171581.3134708
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/VLRVM5KU

Jacob Thebault-Spieker, Daniel Kluver, Maximilian A. Klein, Aaron Halfaker, Brent Hecht, Loren Terveen, and Joseph A. Konstan. 2017. Simulation Experiments on (the Absence of) Ratings Bias in Reputation Systems. Proceedings of the ACM on Human-Computer Interaction 1, CSCW: 1–25. https://doi.org/10.1145/3134736

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3171581.3134736
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RSJ768KW

S. Andrew Sheppard, Julian Turner, Jacob Thebault-Spieker, Haiyi Zhu, and Loren Terveen. 2017. Never Too Old, Cold or Dry to Watch the Sky: A Survival Analysis of Citizen Science Volunteerism. Proceedings of the ACM on Human-Computer Interaction 1, CSCW: 1–21. https://doi.org/10.1145/3134729

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3171581.3134729
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/SXQ3XLY8

S. Andrew Sheppard, Julian Turner, Jacob Thebault-Spieker, Haiyi Zhu, and Loren Terveen. 2017. Never Too Old, Cold or Dry to Watch the Sky: A Survival Analysis of Citizen Science Volunteerism. Proc. ACM Hum.-Comput. Interact. 1, CSCW: 94:1-94:21. https://doi.org/10.1145/3134729

abstract:

CoCoRaHS is a multinational citizen science project for observing precipitation. Like many citizen science projects, volunteer retention is a key measure of engagement and data quality. Through survival analysis, we found that participant age (self-reported at account creation) is a significant predictor of retention. Compared to all other age groups, participants aged 60-70 are much more likely to sign up for CoCoRaHS, and to remain active for several years. We also measured the influence of task difficulty and the relative frequency of rain, finding small but statistically significant and counterintuitive effects. Finally, we confirmed previous work showing that participation levels within the first month are highly predictive of eventual retention. We conclude with implications for observational citizen science projects and crowdsourcing research in general.

links and other notes:

• http://doi.acm.org/10.1145/3134729
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/BVSLIMCF

Peng Zhang, Haiyi Zhu, Tun Lu, Hansu Gu, Wenjian Huang, and Ning Gu. 2017. Understanding Relationship Overlapping on Social Network Sites: A Case Study of Weibo and Douban. Proc. ACM Hum.-Comput. Interact. 1, CSCW: 120:1-120:18. https://doi.org/10.1145/3134755

abstract:

Nowadays people have many overlapping relationships across different social network sites. Will the communication frequency of a relationship on a focal SNS increase or decrease after the corresponding parties begin interacting with each other on a new SNS? What kinds of relationships and parties on the focal SNS are more robust to relationship overlapping across different sites? We conducted a case study on two Chinese popular social network sites (Weibo and Douban). Our results indicate that relationships' interactions on a new SNS have negative effect on the parties' communication frequency on the focal SNS. The communication frequency of older relationships on the focal SNS is more susceptible to the influence of the interacting on the new SNS, while relationships with more common groups and more extensive posts are less likely to be influenced. Our findings imply opportunities for SNS designers to strengthen their existing fragile ties and help users to build much more robust relationships.

links and other notes:

• http://doi.acm.org/10.1145/3134755
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/T8247SKB

Weiwen Leung, Haiyi Zhu, and Joseph A. Konstan. 2017. The Effect of Emotional Cues from the NFL on Wikipedia Contributions. Proc. ACM Hum.-Comput. Interact. 1, CSCW: 66:1-66:21. https://doi.org/10.1145/3134701

abstract:

Exploiting evidence that sporting results affect fans' mood, we analyze whether National Football League game outcomes can affect the contributions of Wikipedia editors who identify as fans of a specific team. We find that the day after a team loses, their fans decrease their contributions towards football-related pages (relative to after a win). Relative decreases are bigger if losses are unexpected, or if losing margins are big. In contrast, unexpected wins do not cause more contributions relative to wins that were not unexpected. Neither do big wins result in more contributions relative to small wins. Additionally, contributions to non-football-related pages are not affected by NFL game results. Our findings add to the literatures on (i) the determinants of individual contributions to peer production communities, (ii) how community dynamics affect user contributions, (iii) the importance of emotions, (iv) the effect of offline events on online behavior, and (v) the applicability of behavioral economics concepts to the HCI literature.

links and other notes:

• http://doi.acm.org/10.1145/3134701
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/GA3BTEZD

Raghav Pavan Karumur, Tien T. Nguyen, and Joseph A. Konstan. 2017. Personality, User Preferences and Behavior in Recommender systems. Information Systems Frontiers: 1–25. https://doi.org/10.1007/s10796-017-9800-0

abstract:

This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns.

links and other notes:

• https://link.springer.com/article/10.1007/s10796-017-9800-0
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FSSNTJG7

Jie Kang, Kyle Condiff, Shuo Chang, Joseph A. Konstan, Loren Terveen, and F. Maxwell Harper. 2017. Understanding How People Use Natural Language to Ask for Recommendations. In Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17), 229–237. https://doi.org/10.1145/3109859.3109873

abstract:

The technical barriers for conversing with recommender systems using natural language are vanishing. Already, there are commercial systems that facilitate interactions with an AI agent. For instance, it is possible to say "what should I watch" to an Apple TV remote to get recommendations. In this research, we investigate how users initially interact with a new natural language recommender to deepen our understanding of the range of inputs that these technologies can expect. We deploy a natural language interface to a recommender system, we observe users' first interactions and follow-up queries, and we measure the differences between speaking- and typing-based interfaces. We employ qualitative methods to derive a categorization of users' first queries (objective, subjective, and navigation) and follow-up queries (refine, reformulate, start over). We employ quantitative methods to determine the differences between speech and text, finding that speech inputs are typically longer and more conversational.

links and other notes:

• https://doi.org/10.1145/3109859.3109873
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NVCDIIUC

Jie Kang, Kyle Condiff, Shuo Chang, Joseph A. Konstan, Loren Terveen, and F. Maxwell Harper. 2017. Understanding How People Use Natural Language to Ask for Recommendations: Query Dataset. https://doi.org/https://doi.org/10.13020/D6VT0F

abstract:

This dataset describes subjects' initial and follow-up queries from the research paper "Understanding How People Use Natural Language to Ask for Recommendations", published in the ACM Conference on Recommender Systems (RecSys), 2017. The data were collected on movielens.org between May 12 and May 24, 2016.

links and other notes:

• http://conservancy.umn.edu/handle/11299/188687
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/G7HEFWDJ

Gloria Mark, Susan Fussell, Cliff Lampe, M. C. Schraefel, Juan Pablo Hourcade, Caroline Appert, Daniel Wigdor, Loren Terveen, and Scooter Morris. 2017. CHI 2017: Explore innovate inspire. In Conference on Human Factors in Computing Systems - Proceedings, iii–iv. Retrieved May 21, 2020 from https://experts.umn.edu/en/publications/chi-2017-explore-innovate-inspire

abstract:

links and other notes:

• https://experts.umn.edu/en/publications/chi-2017-explore-innovate-inspire
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/V2CGPTIW

Mohit Sharma, F. Maxwell Harper, and George Karypis. 2017. Learning from Sets of Items in Recommender Systems. Retrieved from https://www.thinkmind.org/download.php?articleid=eknow_2017_4_10_68011

abstract:

links and other notes:

• best paper award
• https://www.thinkmind.org/download.php?articleid=eknow_2017_4_10_68011
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/G85475PG

Svetlana Yarosh and Stephen Matthew Schueller. 2017. “Happiness Inventors”: Informing Positive Computing Technologies Through Participatory Design With Children. Journal of Medical Internet Research 19, 1: e14. https://doi.org/10.2196/jmir.6822

abstract:

links and other notes:

• http://www.jmir.org/2017/1/e14/
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/EFKX46I5

Sarah McRoberts and Laura Vardoulakis. 2017. Personal Tasks at Work: An Exploration. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17 Companion), 251–254. https://doi.org/10.1145/3022198.3026330

abstract:

Technology has changed the way that people work, socialize, and generally live their lives. Especially, technology has changed the way that those distinct parts of a person's life intermingle. Previous work has largely focused on different styles of work-life balance, and most of the industry contributions to this problem focus on preventing work tasks from leaking into one's personal life. However, little work has been done to investigate the ways that personal tasks impact the work day. We present the results of a survey of 93 employed people, as they relate to personal tasks that people do at work. We consider the context, consequences, and persistency of these tasks, and propose some design implications that could better reinforce the individual boundaries that people have between their work and personal lives.

links and other notes:

• http://doi.acm.org/10.1145/3022198.3026330
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UXNR8EWL

Xinyi Wang, Haiyi Zhu, Yangyun Li, Yu Cui, and Joseph Konstan. 2017. A community rather than a union: Understanding self-organization phenomenon on Mturk and how it impacts Turkers and requesters. 2210–2216.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/DSQJL6P8

Tawanna R. Dillahunt, Xinyi Wang, Earnest Wheeler, HAOFEI CHENG, Brent Hecht, and Haiyi Zhu. 2017. The Sharing Economy in Computing: A Systematic Literature Review. Proc. ACM Hum.-Comput. Interact 1: 1–38.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/KVTNZCR9

Sahar Aseeri, Kyle Condiff, Maximilian Klein, and Baris Unver. 2017. Freedge: Fighting Food Insecurity with Connected Infrastructure. 33–38.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CRGENPGN

Yuan Yao and Svetlana Yarosh. 2017. Vision-Based Classification of Social Gestures in Videochat Sessions. arXiv preprint arXiv:1707.02654.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/LIL229UK

Baris Unver, Alexander Thayer, and Svetlana Yarosh. 2017. Asymmetric device interaction with projector-camera systems. 301–304.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TFVELMTS

Joanne Tarasuik and Jordy Kaufman. 2017. When and why parents involve young children in video communication. Journal of Children and Media 11, 1: 88–106.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/IJWVBG7Q

Stephen M. Schueller, Colleen Stiles-Shields, and Lana Yarosh. 2017. Online treatment and virtual therapists in child and adolescent psychiatry. Child and Adolescent Psychiatric Clinics 26, 1: 1–12.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/KQ8IA6YX

Carrie James, Katie Davis, Linda Charmaraman, Sara Konrath, Petr Slovak, Emily Weinstein, and Lana Yarosh. 2017. Digital life and youth well-being, social connectedness, empathy, and narcissism. Pediatrics 140, Supplement 2: S71–S75.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/GI7IJQM5

Adel Al-Dawood, Norah Abokhodair, and Svetlana Yarosh. 2017. Against Marrying a Stranger: Marital Matchmaking Technologies in Saudi Arabia. 1013–1024.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UJB6Z9HS

Hannah Miller, Daniel Kluver, Jacob Thebault-Spieker, Loren Terveen, and Brent Hecht. 2017. Understanding emoji ambiguity in context: The role of text in emoji-related miscommunication.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ZX3KVITW

Jennifer Filson Moses, Patrick C. Dwyer, Paul Fuglestad, John Kim, Alexander Maki, Mark Snyder, and Loren Terveen. 2017. Encouraging online engagement: The role of interdependent self-construal and social motives in fostering online participation. Personality and Individual Differences.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/VS7UCEX3

Brent Hecht, Loren Terveen, Kate Starbird, Ben Shneiderman, and Jennifer Golbeck. 2017. The 2016 US election and HCI: Towards a research agenda. 1307–1311.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FE7GCDHV

Tien T. Nguyen, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2017. User Personality and User Satisfaction with Recommender Systems. Information Systems Frontiers: 1–17.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/HVKBWRMR

MAXIMILIAN KLEIN, JINHAO ZHAO, JIAJUN NI, ISAAC JOHNSON, BENJAMIN MAKO HILL, and HAIYI ZHU. 2017. Quality Standards, Service Orientation, and Power in Airbnb and Couchsurfing.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FZNR4K9L

Hao Fei Cheng, Bowen Yu, Yeong Hoon Park, and Haiyi Zhu. 2017. ProjectLens: Supporting Project-based Collaborative Learning on MOOCs. 253–256.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/AQ8NPMR8

Lana Yarosh and Pamela Zave. 2017. Locked or Not?: Mental Models of IoT Feature Interaction. 2993–2997.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TNGF79LU

Feargal Kavanagh. 2017. An Evaluation of Collaborative Filtering Methods.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/4RQ36UBU

BOWEN YU, XINYI WANG, ALLEN YILUN LIN, YUQING REN, LOREN TERVEEN, and HAIYI ZHU. 2017. Out With The Old, In With The New? Unpacking Member Turnover in Online Production Groups.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/89RKYAE4

S. ANDREW SHEPPARD, JULIAN TURNER, JACOB THEBAULT-SPIEKER, HAIYI ZHU, and LOREN TERVEEN. 2017. Never Too Old, Cold or Dry to Watch the Sky: A Survival Analysis of Citizen Science Volunteerism.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3VP4SGVT

Andy Hira and Katherine Reilly. 2017. The emergence of the sharing economy: Implications for development. Journal of Developing Societies 33, 2: 175–190.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/9CDFIZRU

Loren Terveen. 2017. SIGCHI supports open exchange of information and freedom of movement. interactions 24, 3: 86–86.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RFRJA3RJ

Loren Terveen. 2017. Empowering the HCI community. interactions 25, 1: 79–79.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/Q8T3W7Z6

Hanna Schäfer, Santiago Hors-Fraile, Raghav Pavan Karumur, André Calero Valdez, Alan Said, Helma Torkamaan, Tom Ulmer, and Christoph Trattner. 2017. Towards Health (Aware) Recommender Systems. 157–161. https://doi.org/10.1145/3079452.3079499

abstract:

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3079452.3079499
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NJJGUWI3

Sarah McRoberts, Haiwei Ma, Andrew Hall, and Svetlana Yarosh. 2017. Share First, Save Later: Performance of Self through Snapchat Stories. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 6902–6911. https://doi.org/10.1145/3025453.3025771

abstract:

As the third most popular social network among millennials, Snapchat is well known for its picture and video messaging system that deletes content after it is viewed. However, the Stories feature of Snapchat offers a different perspective of ephemeral content sharing, with pictures and videos that are available for friends to watch an unlimited number of times for 24 hours. We conduct-ed an in-depth qualitative investigation by interviewing 18 participants and reviewing 14 days of their Stories posts. We identify five themes focused on how participants perceive and use the Stories feature, and apply a Goffmanesque metaphor to our analysis. We relate the Stories medium to other research on self-presentation and identity curation in social media.

links and other notes:

• http://dl.acm.org/citation.cfm?doid=3025453.3025771
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/QQ68Z4PH

Jacob Thebault-Spieker, Loren Terveen, and Brent Hecht. 2017. Towards a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit. Retrieved from http://jacob.thebault-spieker.com/papers/TOCHI_SharingEconomy.pdf

abstract:

Despite the geographically-situated nature of most sharing economy tasks, little attention has been paid to the role that geography plays in the sharing economy. In this article, we help to address this gap in the literature by examining how four key principles from human geography–distance decay, structured variation in population density, mental maps, and “the Big Sort” (spatial homophily)–manifest in sharing economy platforms. We find that these principles interact with platform design decisions to create systemic biases in which the sharing economy is significantly more effective in dense, high socioeconomic status (SES) areas than in low-SES areas and the suburbs. We further show that these results are robust across two sharing economy platforms: UberX and TaskRabbit. In addition to highlighting systemic sharing economy biases, this article more fundamentally demonstrates the importance of considering well-known geographic principles when designing and studying sharing economy platforms.

links and other notes:

• http://jacob.thebault-spieker.com/papers/TOCHI_SharingEconomy.pdf
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/IUV674JG

Ashley Colley, Jacob Thebault-Spieker, Allen Yilun Lin, Donald Degraen, Benjamin Fischman, Jonna Häkkilä, Kate Kuehl, Valentina Nisi, Nuno Jardim Nunes, Nina Wenig, Wenig, Dirk, and Hecht, Brent. 2017. The Geography of Pokémon GO: Beneficial and Problematic Effects on Places and Movement. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Retrieved March 16, 2017 from https://www.researchgate.net/profile/Johannes_Schoening/publication/312936002_The_Geography_of_Pokemon_GO_Beneficial_and_Problematic_Effects_on_Places_and_Movement/links/588a201f45851594678d5216/The-Geography-of-Pokemon-GO-Beneficial-and-Problematic-Effects-on-Places-and-Movement.pdf

abstract:

The widespread popularity of Pokémon GO presents the first opportunity to observe the geographic effects of locationbased gaming at scale. This paper reports the results of a mixed methods study of the geography of Pokémon GO that includes a five-country field survey of 375 Pokémon GO players and a large scale geostatistical analysis of game elements. Focusing on the key geographic themes of places and movement, we find that the design of Pokémon GO reinforces existing geographically-linked biases (eg the game advantage urban areas and neighborhoods with smaller minority populations), that Pokémon GO may have instigated a relatively rare large-scale shift in global human mobility patterns, and that Pokémon GO has geographically linked safety risks, but not those typically emphasized by the media. Our results point to geographic design implications for future systems in this space such as a means through which the geographic biases present in Pokémon GO may be counteracted.

links and other notes:

• https://www.researchgate.net/profile/Johannes_Schoening/publication/312936002_The_Geography_of_Pokemon_GO_Beneficial_and_Problematic_Effects_on_Places_and_Movement/links/588a201f45851594678d5216/The-Geography-of-Pokemon-GO-Beneficial-and-Problematic-Effects-on-Places-and-Movement.pdf
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/5MV5DFPT

Sabirat Rubya and Svetlana Yarosh. 2017. Video-Mediated Peer Support in an Online Community for Recovery from Substance Use Disorders. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 1454–1469. https://doi.org/10.1145/2998181.2998246

abstract:

People in recovery from substance use disorders seek peer support through online health communities like InTheRooms.com (ITR). This community provides the unique opportunity to study video-mediated health peer support online, as it hosts over 100 weekly video meetings for fellowships like Alcoholics Anonymous (AA). We describe two synergistic investigations of participants' use, perceptions, and tensions around video meetings on ITR: an online questionnaire, and in-depth inter-views with active site members. We discuss four themes that may be important to other peer-support health communities: opportunities for video-mediated support, synergy with face-to-face contact, challenge of transparency of norms, and the importance of constructive moderation.

links and other notes:

• http://dl.acm.org/authorize?N23870
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/4997G2UU

Eric Gilbert, Cliff Lampe, Alex Leavitt, Katherine Lo, and Lana Yarosh. 2017. Conceptualizing, Creating, & Controlling Constructive and Controversial Comments: A CSCW Research-athon. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 425–430. https://doi.org/10.1145/3022198.3022663

abstract:

Many social media platforms provide features for commenting, which often provide affordances for people to give feedback to others. Around the theme of advancing research about online comments, this workshop aims to bring together a diverse range of researchers for cross-discipline community building and especially dedicated time for productive, early mixed-methods research. The workshop is structured around a research "hackathon," where participants actively work on producing initial stages of research, theory, and design related to a central dataset (in the case of this workshop, public comments from a historical reddit.com corpus). Participants will form collaborative teams to tackle questions of interest that move forward current thinking around online comment behaviors, participants, quality, and design. The one-day workshop is led by 5 researchers, and it will accommodate up to 32 participants.

links and other notes:

• http://dl.acm.org/authorize?N23870
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/MRCNMC26

Qian Zhao, Gediminas Adomavicius, F. Maxwell Harper, Martijn Willemsen, and Joseph A. Konstan. 2017. Toward Better Interactions in Recommender Systems: Cycling and Serpentining Approaches for Top-N Item Lists. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 1444–1453. https://doi.org/10.1145/2998181.2998211

abstract:

Current recommender systems often show the same most-highly recommended items again and again ignoring the feedback that users neither rate nor click on those items. We conduct an online field experiment to test two ways of manipulating top-N recommendations with the goal of improving user experience: cycling the top-N recommendation based on their past presentation and serpentining the top-N list mixing the best items into later recommendation requests. We find interesting tensions between opt-outs and activities, user perceived accuracy and freshness. Cycling within the same session might be a "love it or hate it" recommender property because users in it have a higher opt-out rate but engage in more activities. Cycling across sessions and serpentining increase user activities without significantly affecting opt-out rates. Users perceive more change and freshness but less accuracy and familiarity. Combining cycling and serpentining does not work as well as each individual manipulation separately. These two ways of manipulations on top-N list demonstrate some attractive properties but also call for innovative approaches to overcome their potential costs.

links and other notes:

• https://dl.acm.org/authorize?N23829
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/KKFC6AAH

Bowen Yu, Yuqing Ren, Loren Terveen, and Haiyi Zhu. 2017. Predicting Member Productivity and Withdrawal from Pre-Joining Attachments in Online Production Groups. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, 1775–1784. https://doi.org/10.1145/2998181.2998227

abstract:

Productive and dedicated members are critical to the success of online production communities like Wikipedia. Many communities organize in subgroups where members voluntarily work on projects of shared interest. In this paper, we investigate how members' pre-joining connections with the subgroup predict their productivity and withdrawal after joining. Drawing insights from attachment theories in social psychology, we examine two types of pre-joining connections: textit{identity-based} attachment (how much members' interests were aligned with the subgroup's topics) and textit{bonds-based} attachment (how much members had interacted with other members of the subgroup). Analyses of 79,704 editors in 1,341 WikiProjects show that 1) both identity-based and bonds-based attachment increased editors' post-joining productivity and reduced their likelihood of withdrawal; 2) identity-based attachment had a stronger effect on boosting direct contributions to articles while bonds-based attachment had a stronger effect on increasing article and project coordination, and reducing member withdrawal.

links and other notes:

• https://dl.acm.org/authorize?N23828
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/HCA9J6KC

Yilun Lin, Bowen Yu, Andrew Hall, and Brent Hecht. 2017. Problematizing and Addressing the Article-as-Concept Assumption in Wikipedia. 2052–2067. https://doi.org/10.1145/2998181.2998274

abstract:

Wikipedia-based studies and systems frequently assume that no two articles describe the same concept. However, in this paper, we show that this article-as-concept assumption is problematic due to editors' tendency to split articles into parent articles and sub-articles when articles get too long for readers (e.g. "Portland, Oregon" and "History of Portland, Oregon" in the English Wikipedia). In this paper, we present evidence that this issue can have significant impacts on Wikipedia-based studies and systems and introduce the sub-article matching problem. The goal of the sub-article matching problem is to automatically connect sub-articles to parent articles to help Wikipedia-based studies and systems retrieve complete information about a concept. We then describe the first system to address the sub-article matching problem. We show that, using a diverse feature set and standard machine learning techniques, our system can achieve good performance on most of our ground truth datasets, significantly outperforming baseline approaches.

links and other notes:

• https://dl.acm.org/authorize?N23827
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/KEIP3SC2

Andrew Hall, Sarah McRoberts, Jacob Thebault-Spieker, Allen Lin, Shilad Sen, Brent Hecht, and Loren Terveen. 2017. Freedom versus Standardization: Structured Data Generation in a Peer Production Community. 12. Retrieved from http://jacob.thebault-spieker.com/papers/CHI17_OSMQual.pdf

abstract:

links and other notes:

• http://jacob.thebault-spieker.com/papers/CHI17_OSMQual.pdf
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/XX3KBTQN

Loren Terveen. 2016. Sustaining our community. interactions 24, 1: 78–78. https://doi.org/10.1145/3021382

abstract:

links and other notes:

• https://dl.acm.org/authorize?N20999
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/EC5DFWX3

Loren Terveen and Aaron Quigley. 2016. SIGCHI’s family of conferences. interactions 23, 5: 70–71. https://doi.org/10.1145/2974647

abstract:

links and other notes:

• http://dl.acm.org/authorize?N12879
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/P8GDIT24

Loren Terveen. 2016. Accessing ACM/SIGCHI publications: open enough? interactions 23, 3: 78–79. https://doi.org/10.1145/2911328

abstract:

links and other notes:

• http://dl.acm.org/authorize?N15168
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2SJX87SI

Molly A. McClellan, Raghav Pavan Karumur, Rachel Isaksson Vogel, Sue V. Petzel, Julie Cragg, Daniel Chan, Julie A. Jacko, François Sainfort, and Melissa A. Geller. 2016. Designing an Educational Website to Improve Quality of Supportive Oncology Care for Women with Ovarian Cancer: An Expert Usability Review and Analysis. International Journal of Human–Computer Interaction 32, 4: 297–307. https://doi.org/10.1080/10447318.2016.1140528

abstract:

A broad-based research team developed a Health Insurance Portability and Accountability Act (HIPAA)-compliant educational website for women with ovarian cancer to improve the quality of supportive oncology care. Prior to a randomized clinical trial of the website, initial usability testing was implemented to evaluate the website. The initial review found that 165/247 checklist items had sufficient information to allow for evaluation with the website achieving an overall score of 63%. By category, the lowest scores were for the Home Page, Task Orientation, Page Layout & Visual Design, and Help, Feedback & Error Tolerance. Major issues thought to potentially impede actual usage were prioritized in redevelopment and the second usability review, conducted by the same expert, saw an improvement in scores. Incorporating usability concepts from the start of development, fulfilling the positive expectations of end-users, and identifying the technical and personal factors that optimize use may greatly enhance the usage of health websites.

links and other notes:

• PMID: 27110082
• https://doi.org/10.1080/10447318.2016.1140528
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/YC3NTYVH

Molly A. McClellan, Raghav Pavan Karumur, Rachel Isaksson Vogel, Sue V. Petzel, Julie Cragg, Daniel Chan, Julie A. Jacko, François Sainfort, and Melissa A. Geller. 2016. Designing an Educational Website to Improve Quality of Supportive Oncology Care for Women with Ovarian Cancer: An Expert Usability Review and Analysis. International Journal of Human-Computer Interaction 32, 4: 297–307. https://doi.org/10.1080/10447318.2016.1140528

abstract:

links and other notes:

• http://www.tandfonline.com/doi/full/10.1080/10447318.2016.1140528
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/89AZ9U3Q

Svetlana Yarosh and Shankar Krishnan. 2016. System and method for providing separate communication zones in a large format videoconference. Retrieved May 4, 2016 from http://www.freepatentsonline.com/y2016/0080691.html

abstract:

A system that incorporates the subject disclosure performs, for example, displaying a video image of a remote scene at a display surface, wherein the remote scene is remote from the display surface. Overlapping video images are obtained from different vantage points of a local scene observable from the display surface. A composite video image is generated of the local scene from the video images and forwarded to the remote location. A first audio signal is generated representing first sounds associated with a first region of the local scene without representing other sounds associated with a second region of the local scene. The first audio signal is forwarded to audio processing equipment at the remote location to present the first sounds at a first region of the remote scene at the remote location without presenting the first sounds at a second region of the remote scene. Other embodiments are disclosed.

links and other notes:

• US Patent 20,160,080,691
• http://www.freepatentsonline.com/y2016/0080691.html
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/P968D9QI

Raghav Pavan Karumur, Tien T. Nguyen, and Joseph A. Konstan. 2016. Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16), 139–142. https://doi.org/10.1145/2959100.2959140

abstract:

Prior work relevant to incorporating personality into recommender systems falls into two categories: social science studies and algorithmic ones. Social science studies of preference have found only small relationships between personality and category preferences, whereas, algorithmic approaches found a little improvement when incorporating personality into recommendations. As a result, despite good reasons to believe personality assessments should be useful in recommenders, we are left with no substantial demonstrated impact. In this work, we start with user data from a live recommender system, but study category-by-category variations in preference (both rating levels and distribution) across different personality types. By doing this, we hope to isolate specific areas where personality is most likely to provide value in recommender systems, while also modeling an analytic process that can be used in other domains. After controlling for the family-wise error rate, we find that High Agreeableness users rate at least 0.5 stars higher on a 5-star scale compared to low Agreeableness users. We also find differences in consumption in four different personality types between people who manifested high and low levels of that personality.

links and other notes:

• http://doi.acm.org/10.1145/2959100.2959140
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/8N4PDVXY

Dirk Wenig, Alexander Steenbergen, Johannes Schöning, Brent Hecht, and Rainer Malaka. 2016. ScrollingHome: Bringing Image-based Indoor Navigation to Smartwatches. https://doi.org/10.1145/2935334.2935373

abstract:

Providing pedestrian navigation instructions on small screens is a challenging task due to limited screen space. As image-based approaches for navigation have been successfully proven to outperform map-based navigation on mobile devices, we propose to bring image-based navigation to smartwatches. We contribute a straightforward pipeline to easily create image-based indoor navigation instructions that allow users to freely navigate in indoor environments without any localization infrastructure and with minimal user input on the smartwatch. In a user study, we show that our approach outperforms the current state-of-the art application in terms of task completion time, perceived task load and perceived usability. In addition, we did not find an indication that there is a need to provide explicit directional instructions for image-based navigation on small screens.

links and other notes:

• https://dl.acm.org/authorize?N20990
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/KZ3ZIADW

Qian Zhao, Shuo Chang, F. Maxwell Harper, and Joseph A. Konstan. 2016. Gaze Prediction for Recommender Systems. In Proceedings of the 10th ACM Conference on Recommender Systems, 131–138. https://doi.org/10.1145/2959100.2959150

abstract:

As users browse a recommender system, they systematically consider or skip over much of the displayed content. It seems obvious that these eye gaze patterns contain a rich signal concerning these users' preferences. However, because eye tracking data is not available to most recommender systems, these signals are not widely incorporated into personalization models. In this work, we show that it is possible to predict gaze by combining easily-collected user browsing data with eye tracking data from a small number of users in a grid-based recommender interface. Our technique is able to leverage a small amount of eye tracking data to infer gaze patterns for other users. We evaluate our prediction models in MovieLens -- an online movie recommender system. Our results show that incorporating eye tracking data from a small number of users significantly boosts accuracy as compared with only using browsing data, even though the eye-tracked users are different from the testing users (e.g. AUC=0.823 vs. 0.693 in predicting whether a user will fixate on an item). We also demonstrate that Hidden Markov Models (HMMs) can be applied in this setting; they are better than linear models in predicting fixation probability and capturing the interface regularity through Bayesian inference (AUC=0.823 vs. 0.757).

links and other notes:

• http://dl.acm.org/authorize?N12876
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ZZEGZQUM

Qian Zhao, Zihong Huang, F. Maxwell F. Harper, Loren G Terveen, and Joseph A. Konstan. 2016. Precision CrowdSourcing: Closing the Loop to Turn Information Consumers into Information Contributors. In 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 1613–1623. https://doi.org/10.1145/2818048.2819957

abstract:

We introduce a theoretical framework called precision crowdsourcing whose goal is to help turn online information consumers into information contributors. The framework looks at the timing and nature of the requests made of users and the feedback provided to users with the goal of increasing long-term contribution and engagement in the site or system. We present the results of a field experiment in which almost 3000 users were asked to tag movies (plus a null control group) as we varied the selection of task (popular/obscure), timing of requests (immediate or varying delays), and relational rhetoric (neutral, system reciprocal, other users reciprocal) of the requests. We found that asking increases tags provided overall, though asking generally decreases the provision of unprompted tags. Users were more likely to comply with our request when we asked them to tag obscure movies and when we used reciprocal request rhetoric.

links and other notes:

• http://dl.acm.org/authorize?N00084
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RR8IGT42

Shuo Chang, F. Maxwell Harper, and Loren Gilbert Terveen. 2016. Crowd-Based Personalized Natural Language Explanations for Recommendations. 175–182. https://doi.org/10.1145/2959100.2959153

abstract:

links and other notes:

• http://dl.acm.org/authorize?N12870
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/S8TSWVXR

Maximilian Klein, Harsh Gupta, Vivek Rai, Piotr Konieczny, and Haiyi Zhu. 2016. Monitoring the Gender Gap with Wikidata Human Gender Indicators. In Proceedings of the 12th International Symposium on Open Collaboration, 1–9. https://doi.org/10.1145/2957792.2957798

abstract:

The gender gap in Wikipedia's content, specifically in the representation of women in biographies, is well-known but has been difficult to measure. Furthermore the impacts of efforts to address this gender gap have received little attention. To investigate we utilise Wikidata, the database that feeds Wikipedia, and introduce the "Wikidata Human Gender Indicators" (WHGI), a free and open source, longitudinal, biographical dataset monitoring gender disparities across time, space, culture, occupation and language. Through these lenses we show how the representation of women is changing along 11 dimensions. Validations of WHGI are presented against three exogenous datasets: the world's historical population, "traditional" gender-disparity indices (GDI, GEI, GGGI and SIGI), and occupational gender according to the US Bureau of Labor Statistics. Furthermore, to demonstrate its general use in research, we revisit previously published findings on Wikipedia's gender bias that can be strengthened by WHGI.

links and other notes:

• http://dl.acm.org/authorize?N26748
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/4SJF2HGD

Sarah McRoberts, Elizabeth Bonsignore, Tamara Peyton, and Svetlana Yarosh. 2016. Do It for the Viewers!: Audience Engagement Behaviors of Young YouTubers. In Proceedings of the The 15th International Conference on Interaction Design and Children, 334–343. https://doi.org/10.1145/2930674.2930676

abstract:

Online user-generated video sharing communities, such as YouTube, are becoming more popular than conventional studio-produced content. These communities provide every user with the opportunity to create and promote their own video content---a compelling venue for children and teenagers to share their stories and voices. In this study, we investigate the practices of youth video creators on YouTube. To do so, we conducted a content analysis of the audience engagement practices of 100 youth author channels, comparing them to adult and professional YouTubers when appropriate. We found that most youth authors are aware of and actively engage with their imaginary or real audiences on YouTube. They emulate the conversational and audience engagement practices seen in professional quality YouTube channels, but may not have the video editing or other meta-content skills or experience to do so. Based on our findings, we point to a number of implications for future research and design in this space.

links and other notes:

• http://dl.acm.org/citation.cfm?doid=2930674.2930676
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2JB5SBER

Raghav Pavan Karumur and Joseph A. Konstan. 2016. Relating Newcomer Personality to Survival and Activity in Recommender Systems. In Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization, 195–205. https://doi.org/10.1145/2930238.2930246

abstract:

In this work, we explore the degree to which personality information can be used to model newcomer retention, investment, intensity of engagement, and distribution of activity in a recommender community. Prior work shows that Big-Five Personality traits can explain variation in user behavior in other contexts. Building on this, we carry out and report on an analysis of 1008 MovieLens users with identified personality profiles. We find that Introverts and low Agreeableness users are more likely to survive into the second and subsequent sessions compared to their respective counterparts; Introverts and low Conscientiousness users are a significantly more active population compared to their respective counterparts; High Openness and High Neuroticism users contribute (tag) significantly more compared to their counterparts, but their counterparts consume (browse and bookmark) more; and low Agreeableness users are more likely to rate whereas high Agreeableness users are more likely to tag. These results show how modeling newcomer behavior from user personality can be useful for recommender systems designers as they customize the system to guide people towards tasks that need to be done or tasks the users will find rewarding and also decide which users to invest retention efforts in.

links and other notes:

• http://dl.acm.org/authorize?N12877
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/SZS6CSE8

Raghav Pavan Karumur, Tien T. Nguyen, and Joseph A. Konstan. 2016. Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens. In Proceedings of the 10th ACM Conference on Recommender Systems, 139–142. https://doi.org/10.1145/2959100.2959140

abstract:

Prior work relevant to incorporating personality into recommender systems falls into two categories: social science studies and algorithmic ones. Social science studies of preference have found only small relationships between personality and category preferences, whereas, algorithmic approaches found a little improvement when incorporating personality into recommendations. As a result, despite good reasons to believe personality assessments should be useful in recommenders, we are left with no substantial demonstrated impact. In this work, we start with user data from a live recommender system, but study category-by-category variations in preference (both rating levels and distribution) across different personality types. By doing this, we hope to isolate specific areas where personality is most likely to provide value in recommender systems, while also modeling an analytic process that can be used in other domains. After controlling for the family-wise error rate, we find that High Agreeableness users rate at least 0.5 stars higher on a 5-star scale compared to low Agreeableness users. We also find differences in consumption in four different personality types between people who manifested high and low levels of that personality.

links and other notes:

• http://dl.acm.org/authorize?N12875
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UWSNJ7MH

Hannah Miller, Jacob Thebault-Spieker, Shuo Chang, Isaac Johnson, Loren Terveen, and Brent Hecht. 2016. “Blissfully happy” or “ready to fight”: Varying Interpretations of Emoji. ICWSM’16. Retrieved July 6, 2016 from http://www-users.cs.umn.edu/~bhecht/publications/ICWSM2016_emoji.pdf

abstract:

Emoji are commonly used in modern text communication. However, as graphics with nuanced details, emoji may be open to interpretation. Emoji also render differently on different viewing platforms (e.g., Apple’s iPhone vs. Google’s Nexus phone), potentially leading to communication errors. We explore whether emoji renderings or differences across platforms give rise to diverse interpretations of emoji. Through an online survey, we solicit people’s interpretations of a sample of the most popular emoji characters, each rendered for multiple platforms. Both in terms of sentiment and semantics, we analyze the variance in interpretation of the emoji, quantifying which emoji are most (and least) likely to be misinterpreted. In cases in which participants rated the same emoji rendering, they disagreed on whether the sentiment was positive, neutral, or negative 25% of the time. When considering renderings across platforms, these disagreements only increase. Overall, we find significant potential for miscommunication, both for individual emoji renderings and for different emoji renderings across platforms.

links and other notes:

• http://www-users.cs.umn.edu/~bhecht/publications/ICWSM2016_emoji.pdf
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/P3I7WCHE

Svetlana Yarosh, Elizabeth Bonsignore, Sarah McRoberts, and Tamara Peyton. 2016. YouthTube: Youth Video Authorship on YouTube and Vine. In 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 1421–1435. https://doi.org/10.1145/2818048.2819961

abstract:

What kinds of content do children and teenagers author and share on public video platforms? We approached this question through a qualitative directed content analysis of over 250 youth-authored videos filtered by crowdworkers from public videos on YouTube and Vine. We found differences between YouTube and Vine platforms in terms of the age of the youth authors, the type of collaborations witnessed in the videos, and the significantly greater amount of violent, sexual, and obscene content on Vine. We also highlight possible differences in how adults and youths approach online video sharing. Specifically, we consider that adults may view online video as an archive to keep precious memories of everyday life with their family, friends, and pets, humorous moments, and special events, while children and teenagers treat online video as a stage to perform, tell stories, and express their opinions and identities in a performative way.

links and other notes:

• http://dl.acm.org/authorize?N09485
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/8USV2D8E

Svetlana Yarosh, Sarita Schoenebeck, Shreya Kothaneth, and Elizabeth Bales. 2016. “Best of Both Worlds”: Opportunities for Technology in Cross-Cultural Parenting. In 2016 CHI Conference on Human Factors in Computing Systems, 635–647. https://doi.org/10.1145/2858036.2858210

abstract:

Families are becoming more culturally heterogeneous due to a rise in intermarriage, geographic mobility, and access to a greater diversity of cultural perspectives online. Investigating the challenges of cross-cultural parenting can help us support this growing demographic, as well as better understand how families integrate and negotiate advice from diverse online and offline sources in making parenting decisions. We interviewed parents from 18 families to understand the practices they adopt to meet the challenges of cross-cultural parenting. We investigated how these families respond to conflicts while integrating diverse cultural views, as well as how they utilize the wealth of parenting resources available online in navigating these tasks. We identify five themes focused on how these families find and evaluate advice, connect with social support, resolve intra-family tensions, incorporate multicultural practices, and seek out diverse views. Based on our findings, we contribute three implications for design and translations of these implications to concrete technology ideas that aim to help families better integrate multiple cultures into everyday life.

links and other notes:

• http://dl.acm.org/authorize?N04840
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/MVAET6WM

Baris Unver, Sarah A. McRoberts, Sabirat Rubya, Haiwei Ma, Zuoyi Zhang, and Svetlana Yarosh. 2016. ShareTable Application for HP Sprout. In 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, 3784–3787. https://doi.org/10.1145/2851581.2890252

abstract:

Projector-camera (pro-cam) systems create virtual environments for remote interaction with audio and video support. However, previous pro-cam systems require custom hardware and thus have not been used at-scale in the field. In this work, we present a pro-cam application to reinforce social relationships for an off-the-shelf pro-cam system. The ShareTable application users can use video chat in conjunction with sharing tabletop video, allowing them to connect while playing and interacting together.

links and other notes:

• http://dl.acm.org/authorize?N04849
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/DCN96G86

Haiyi Zhu, Sauvik Das, Yiqun Cao, Shuang Yu, Aniket Kittur, and Robert Kraut. 2016. A Market in Your Social Network: The Effects of Extrinsic Rewards on Friendsourcing and Relationships. In 2016 CHI Conference on Human Factors in Computing Systems, 598–609. https://doi.org/10.1145/2858036.2858396

abstract:

Friendsourcing consists of broadcasting questions and help requests to friends on social networking sites. Despite its potential value, friendsourcing requests often fall on deaf ears. One way to improve response rates and motivate friends to undertake more effortful tasks may be to offer extrinsic rewards, such as money or a gift, for responding to friendsourcing requests. However, past research suggests that these extrinsic rewards can have unintended consequences, including undermining intrinsic motivations and undercutting the relationship between people. To explore the effects of extrinsic reward on friends' response rate and perceived relationship, we conducted an experiment on a new friendsourcing platform - Mobilyzr. Results indicate that large extrinsic rewards increase friends' response rates without reducing the relationship strength between friends. Additionally, the extrinsic rewards allow requesters to explain away the failure of friendsourcing requests and thus preserve their perceptions of relationship ties with friends.

links and other notes:

• http://dl.acm.org/authorize?N04848
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ZDRSQ3IH

Isaac L. Johnson, Yilun Lin, Toby Jia-Jun Li, Andrew Hall, Aaron Halfaker, Johannes Schöning, and Brent Hecht. 2016. Not at Home on the Range: Peer Production and the Urban/Rural Divide. In 2016 CHI Conference on Human Factors in Computing Systems, 13–25. https://doi.org/10.1145/2858036.2858123

abstract:

Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. "bots"). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions.

links and other notes:

• http://dl.acm.org/authorize?N04843
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/I5D6QBC2

Marcus Soll, Philipp Naumann, Johannes Schöning, Pavel Samsonov, and Brent Hecht. 2016. Helping Computers Understand Geographically-Bound Activity Restrictions. In 2016 CHI Conference on Human Factors in Computing Systems, 2442–2446. https://doi.org/10.1145/2858036.2858053

abstract:

The lack of certain types of geographic data prevents the development of location-aware technologies in a number of important domains. One such type of "unmapped" geographic data is space usage rules (SURs), which are defined as geographically-bound activity restrictions (e.g. "no dogs", "no smoking", "no fishing", "no skateboarding"). Researchers in the area of human-computer interaction have recently begun to develop techniques for the automated mapping of SURs with the aim of supporting activity planning systems (e.g. one-touch "Can I Smoke Here?" apps, SUR-aware vacation planning tools). In this paper, we present a novel SUR mapping technique -- SPtP -- that outperforms state-of-the-art approaches by 30% for one of the most important components of the SUR mapping pipeline: associating a point observation of a SUR (e.g. a 'no smoking' sign) with the corresponding polygon in which the SUR applies (e.g. the nearby park or the entire campus on which the sign is located). This paper also contributes a series of new SUR benchmark datasets to help further research in this area.

links and other notes:

• http://dl.acm.org/authorize?N04842
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/EQSUK4HT

Isaac L. Johnson, Subhasree Sengupta, Johannes Schöning, and Brent Hecht. 2016. The Geography and Importance of Localness in Geotagged Social Media. In 2016 CHI Conference on Human Factors in Computing Systems, 515–526. https://doi.org/10.1145/2858036.2858122

abstract:

Geotagged tweets and other forms of social media volunteered geographic information (VGI) are becoming increasingly critical to many applications and scientific studies. An important assumption underlying much of this research is that social media VGI is "local", or that its geotags correspond closely with the general home locations of its contributors. We demonstrate through a study on three separate social media communities (Twitter, Flickr, Swarm) that this localness assumption holds in only about 75% of cases. In addition, we show that the geographic contours of localness follow important sociodemographic trends, with social media in, for instance, rural areas and older areas, being substantially less local in character (when controlling for other demographics). We demonstrate through a case study that failure to account for non-local social media VGI can lead to misrepresentative results in social media VGI-based studies. Finally, we compare the methods for determining localness, finding substantial disagreement in certain cases, and highlight new best practices for social media VGI-based studies and systems.

links and other notes:

• http://dl.acm.org/authorize?N04841
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FG3W3AUX

Michael D. Ekstrand and Michael Ludwig. 2016. Dependency Injection with Static Analysis and Context-Aware Policy. The Journal of Object Technology 15, 1: 1:1. https://doi.org/10.5381/jot.2016.15.5.a1

abstract:

The dependency injection design pattern improves the configurability, testability, and maintainability of object-oriented applications by decoupling components from both the concrete implementations of their dependencies and the strategy employed to select those implementations. In recent years, a number of libraries have emerged that provide automated support for constructing and connecting dependency-injected objects. Our experience developing systems with these tools has led us to identify two shortcomings of existing dependency injection solutions: the mechanisms for specifying component implementations often make it difficult to write and configure systems of arbitrarily-composable components, and the toolkit implementations often provide limited capabilities for inspection and static analysis of the object graphs of dependency-injected systems. We present Grapht, an new dependency injection container for Java that addresses these issues by providing context-aware policy, allowing component implementation decisions to depend on where in the object graph a component is required, and using a design that allows for static analysis of configured object graphs. To achieve its objectives, Grapht is built on a mathematical representation of dependency injection and object graphs that facilitates static analysis and straightforward implementation, and forms a basis for further consideration of the capabilities of dependency injection. The mathematical representation includes context-aware policy that we show to be strictly more expressive than the qualified dependencies used in many current toolkits. We demonstrate the utility of our approach with a case study showing how Grapht has aided in the development of the LensKit recommender systems toolkit.

links and other notes:

• http://www.jot.fm/contents/issue_2016_01/article1.html
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/MJNWNUZM

Shuo Chang, F. Maxwell Harper, Lingfei He, and Loren G. Terveen. 2016. CrowdLens: Experimenting with Crowd-Powered Recommendation and Explanation. In Tenth International AAAI Conference on Web and Social Media. Retrieved May 23, 2016 from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/view/13111

abstract:

Recommender systems face several challenges, e.g., recommending novel and diverse items and generating helpful explanations. Where algorithms struggle, people may excel. We therefore designed CrowdLens to explore different workflows for incorporating people into the recommendation process. We did an online experiment, finding that: compared to a state-of-the-art algorithm, crowdsourcing workflows produced more diverse and novel recommendations favored by human judges; some crowdworkers produced high-quality explanations for their recommendations, and we created an accurate model for identifying high-quality explanations; volunteers from an online community generally performed better than paid crowdworkers, but appropriate algorithmic support erased this gap. We conclude by reflecting on lessons of our work for those considering a crowdsourcing approach and identifying several fundamental issues for future work.

links and other notes:

• http://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/view/13111
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RUZD3JEC

Haiyi Zhu, Robert E Kraut, and Aniket Kittur. 2016. A Contingency View of Transferring and Adapting Best Practices within Online Communities. In 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 727–741. https://doi.org/10.1145/2818048.2819976

abstract:

Online communities, much like companies in the business world, often need to transfer “best practices” internally from one unit to another to improve their performance. Organizational scholars disagree about how much a recipient unit should modify a best practice when incorporating it. Some evidence indicates that modifying a practice that has been successful in one environment will introduce problems, undercut its effectiveness and harm the performance of the recipient unit. Other evidence, though, suggests that recipients need to adapt the practice to fit their local environment. The current research introduces a contingency perspective on practice transfer, holding that the value of modifications depends on when they are introduced and who introduces them. Empirical research on the transfer of a quality-improvement practice between projects within Wikipedia shows that modifications are more helpful if they are introduced after the receiving project has had experience with the imported practice. Furthermore, modifications are more effective if they are introduced by members who have experience in a variety of other projects.

links and other notes:

• http://dl.acm.org/authorize?N00246
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/HIAU92BU

Wenjian Huang, Tun Lu, Haiyi Zhu, Guo Li, and Ning Gu. 2016. Effectiveness of Conflict Management Strategies in Peer Review Process of Online Collaboration Projects. In 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 715–726. https://doi.org/10.1145/2818048.2819950

abstract:

In online collaboration projects, conflicts often arise in the peer review process, due to the disagreement over whether one’s contribution should be accepted. These conflicts generally have detrimental effects on contributors’ continuing participation in the community. Few studies have investigated how to manage these conflicts effectively. This paper aims to examine the effectiveness of three strategies – rational explanation, constructive suggestion, and social encouragement – in managing conflicts. In an analysis of 170 online software development projects, we investigated how different conflict management strategies aimed at handling contributors’ arguments during the peer review process influenced their subsequent participation in the projects. The results show that (i) conflicts significantly increase contributors’ likelihood of leaving the communities; (ii) neither rational explanations nor social encouragement could reduce the negative consequences of conflicts; (iii) only constructive suggestions have a positive effect in retaining the contributors.

links and other notes:

• http://dl.acm.org/authorize?N00245
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/N39CFT4C

Yuan Yao and Svetlana E. Yarosh. 2016. Group Finder: Finding the “Right”: Online Support Groups for People in Recovery. In 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, 445–448. https://doi.org/10.1145/2818052.2869091

abstract:

Nowadays, more people in recovery choose to seek help from online support groups. This paper presents Group Finder, a content-based online support groups recommender that makes suggestions by using the measurements of post contents in social and psychological dimensions.

links and other notes:

• http://dl.acm.org/authorize?N00003
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NQC9BKQS

Thanh-Mai Phan and Svetlana E. Yarosh. 2016. Sustaining Reciprocity: Generating Social Capital within Peer-Support Communities. In 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, 369–372. https://doi.org/10.1145/2818052.2869115

abstract:

The concept of reciprocity is embedded into community. A successful online peer-support community is characterized by its members actively giving and receiving support, namely in the form of advice and encouragement. In this paper, we investigate incentives for sustaining generalized reciprocity and explore its relationship with social capital on InTheRooms.com, an online recovery network.

links and other notes:

• http://dl.acm.org/authorize?N00002
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/95EHBMNS

Jialun Jiang and Svetlana E. Yarosh. 2016. Why Do Teammates Hate Me? Cross-Cultural Tensions and Social Dynamics in Online Games. In 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, 301–304. https://doi.org/10.1145/2818052.2869087

abstract:

We discuss the multi-ethnic tension and social dynamics behind toxicity in online game Dota 2. We present a player survey and preliminary analysis of user-generated keywords and future work in interpreting the survey results.

links and other notes:

• http://dl.acm.org/authorize?N00001
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/K6X9CE7N

Timothy Pallarino, Aaron E. Free, Katrina Mutuc, and Svetlana Yarosh. 2016. Feeling Distance: An Investigation of Mediated Social Touch Prototypes. In 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, 361–364. https://doi.org/10.1145/2818052.2869124

abstract:

Physical distance presents a challenge for building and maintaining relationships. With recent work showing the effectiveness of both visual and haptic feedback in supporting interpersonal touch over a distance, such technologies look to bridge this gap and improve existing communication technologies. In this project, we explore the potential role of shape-shifting displays for doing so. We present two prototypes, one using linear slide potentiometers and the other using linear actuators, that incorporate these forms of feedback to facilitate mediated social touch. We present the benefits and drawbacks of each system, and conclude that, for a system focused on collaboration and synchronous communication, linear actuators may be better suited due to their load capacity and precision.

links and other notes:

• http://dl.acm.org/authorize?N00000
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/4TQXKPS5

Tawanna R. Dillahunt, Airi Lampinen, Jacki O’Neill, Loren Terveen, and Cory Kendrick. 2016. Does the Sharing Economy do any Good? In 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, 197–200. https://doi.org/10.1145/2818052.2893362

abstract:

Despite the benefits offered by sharing economy, researchers have identified several challenges preventing disadvantaged groups (e.g. low socioeconomic status, un(der)employed and/or users from emerging regions) from receiving the same level of benefits as those from advantaged populations. This panel brings researchers from the sharing economy and mobile crowdsourcing space whose research has identified unique challenges for underserved populations. We consider the opportunities offered by these platforms to disadvantaged communities and examine to what extent these platforms instead may recreate disadvantage, as well as the workarounds communities employ to make these platforms work for them. We examine the opportunities for the CSCW community to address these challenges going forward.

links and other notes:

• http://dl.acm.org/authorize?N00091
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/68CCZTA6

Raghav Pavan Karumur, Tien T. Nguyen, and Joseph A. Konstan. 2016. Early Activity Diversity: Assessing Newcomer Retention from First-Session Activity. In 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing, 594–607. https://doi.org/10.1145/2818048.2820009

abstract:

Online communities suffer serious newcomer attrition. This paper explores whether and how early activity diversity – the degree to which a newcomer engages in a wide range of a site’s activities in the first session – is associated with their longevity. We introduce a metric (DSCORE) to characterize early activity diversity in online sites and run our analyses on an online community ‘MovieLens’. We find that DSCORE is significant both by itself and in conjunction with a measure of quantity of activity in predicting longevity. This finding is robust to different measures of longevity (aggregate number of sessions and attritions after sessions 1, 5, and 10). The immediate implication is an effective classifier for identifying users with higher (or lower) expected longevity from the first-session activity. We also find DSCORE is more useful than a traditional measure of measuring diversity such as the Gini-Simpson index. We conclude by discussing how early activity diversity may be more broadly effective in supporting design and management of online communities.

links and other notes:

• http://dl.acm.org/authorize?N00083
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/8ZTJ6IN5

Loren Terveen. 2015. HCI without borders? interactions 23, 1: 86–86. https://doi.org/10.1145/2856515

abstract:

links and other notes:

• http://dl.acm.org/authorize?N00092
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/GJNKZS7W

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4: 19:1-19:19. https://doi.org/10.1145/2827872

abstract:

The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.

links and other notes:

• author version: http://files.grouplens.org/papers/harper-tiis2015.pdf
• http://dl.acm.org/authorize?N08710
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/I8IHQBTU

Loren Terveen. 2015. Greetings from the new SIGCHI leadership. interactions 22, 5: 77–77. https://doi.org/10.1145/2814383

abstract:

links and other notes:

• http://dl.acm.org/authorize?N00016
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/EAKUMAC5

Joseph A. Konstan and Jack W. Davidson. 2015. Should Conferences Meet Journals and Where?: A Proposal for “PACM.” Commun. ACM 58, 9: 5–5. https://doi.org/10.1145/2811400

abstract:

links and other notes:

• http://dl.acm.org/authorize?N08711
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ZV2VK7RR

Aaron Halfaker, Os Keyes, Daniel Kluver, Jacob Thebault-Spieker, Tien Nguyen, Kate Grandprey-Shores, Anuradha Uduwage, and Morten Warncke-Wang. 2015. User Session Identification Based on Strong Regularities in Inter-activity Time. In Proceedings of the 24th International Conference on World Wide Web (WWW ’15), 410–418. https://doi.org/10.1145/2736277.2741117

abstract:

Session identification is a common strategy used to develop metrics for web analytics and perform behavioral analyses of user-facing systems. Past work has argued that session identification strategies based on an inactivity threshold is inherently arbitrary or has advocated that thresholds be set at about 30 minutes. In this work, we demonstrate a strong regularity in the temporal rhythms of user initiated events across several different domains of online activity (incl. video gaming, search, page views and volunteer contributions). We describe a methodology for identifying clusters of user activity and argue that the regularity with which these activity clusters appear implies a good rule-of-thumb inactivity threshold of about 1 hour. We conclude with implications that these temporal rhythms may have for system design based on our observations and theories of goal-directed human activity.

links and other notes:

• https://doi.org/10.1145/2736277.2741117
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/68DGTKRP

Joseph A. Konstan and Jack W. Davidson. 2015. Charting the future: scholarly publishing in CS. Communications of the ACM 58, 4: 5–5. https://doi.org/10.1145/2742646

abstract:

links and other notes:

• http://dl.acm.org/authorize?N00093
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/43G99KZ7

Jeremy A. Grey, Joseph Konstan, Alex Iantaffi, J. Michael Wilkerson, Dylan Galos, and B. R. Simon Rosser. 2015. An Updated Protocol to Detect Invalid Entries in an Online Survey of Men Who Have Sex with Men (MSM): How Do Valid and Invalid Submissions Compare? AIDS and Behavior 19, 10: 1928–1937. https://doi.org/10.1007/s10461-015-1033-y

abstract:

Researchers use protocols to screen for suspicious survey submissions in online studies. We evaluated how well a de-duplication and cross-validation process detected invalid entries. Data were from the Sexually Explicit Media Study, an Internet-based HIV prevention survey of men who have sex with men. Using our protocol, 146 (11.6 %) of 1254 entries were identified as invalid. Most indicated changes to the screening questionnaire to gain entry (n = 109, 74.7 %), matched other submissions’ payment profiles (n = 56, 41.8 %), or featured an IP address that was recorded previously (n = 43, 29.5 %). We found few demographic or behavioral differences between valid and invalid samples, however. Invalid submissions had lower odds of reporting HIV testing in the past year (OR 0.63), and higher odds of requesting no payment compared to check payments (OR 2.75). Thus, rates of HIV testing would have been underestimated if invalid submissions had not been removed, and payment may not be the only incentive for invalid participation.

links and other notes:

• http://link.springer.com/10.1007/s10461-015-1033-y
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RGQZR66N

Isaac Johnson and Brent Hecht. 2015. Structural Causes of Bias in Crowd-derived Geographic Information: Towards a Holistic Understanding. AAAI Spring Symposium on Observational Studies through Social Media and Other Human-Generated Content.

abstract:

A critical subset of human-generated content is that which is geographically referenced. The spatial context of these data has enabled a new class of observational studies and tech-nologies. Unfortunately, researchers have identified a num-ber of biases in these datasets (e.g. urban/rural) that threaten their utility. However, these findings have been highly spe-cialized, focusing on single datasets and dimensions of bias. This extended abstract seeks to begin the process of synthe-sizing a cohesive understanding of the structural causes of bias in geographically-referenced human content. We out-line five cross-cutting causational factors, as well as intro-duce a novel framework that aids in understanding these factors. In doing so, we hope to initiate a discussion that moves the literature on bias in geographic content towards one focused on systemic issues. This would allow the antic-ipation of bias in unseen datasets and, importantly, enable research towards systemic solutions.

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/9BPXS8I2

Shuo Chang, F. Maxwell Harper, and Loren Terveen. 2015. Using Groups of Items for Preference Elicitation in Recommender Systems. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’15), 1258–1269. https://doi.org/10.1145/2675133.2675210

abstract:

To achieve high quality initial personalization, recommender systems must provide an efficient and effective process for new users to express their preferences. We propose that this goal is best served not by the classical method where users begin by expressing preferences for individual items - this process is an inefficient way to convert a user's effort into improved personalization. Rather, we propose that new users can begin by expressing their preferences for groups of items. We test this idea by designing and evaluating an interactive process where users express preferences across groups of items that are automatically generated by clustering algorithms. We contribute a strategy for recommending items based on these preferences that is generalizable to any collaborative filtering-based system. We evaluate our process with both offline simulation methods and an online user experiment. We find that, as compared with a baseline rate-15-items interface, (a) users are able to complete the preference elicitation process in less than half the time, and (b) users are more satisfied with the resulting recommended items. Our evaluation reveals several advantages and other trade-offs involved in moving from item-based preference elicitation to group-based preference elicitation.

links and other notes:

• http://doi.acm.org/10.1145/2675133.2675210
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3QJFBKZR

Michael D. Ekstrand, Daniel Kluver, F. Maxwell Harper, and Joseph A. Konstan. 2015. Letting Users Choose Recommender Algorithms: An Experimental Study. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15), 11–18. https://doi.org/10.1145/2792838.2800195

abstract:

Recommender systems are not one-size-fits-all; different algorithms and data sources have different strengths, making them a better or worse fit for different users and use cases. As one way of taking advantage of the relative merits of different algorithms, we gave users the ability to change the algorithm providing their movie recommendations and studied how they make use of this power. We conducted our study with the launch of a new version of the MovieLens movie recommender that supports multiple recommender algorithms and allows users to choose the algorithm they want to provide their recommendations. We examine log data from user interactions with this new feature to under-stand whether and how users switch among recommender algorithms, and select a final algorithm to use. We also look at the properties of the algorithms as they were experienced by users and examine their relationships to user behavior. We found that a substantial portion of our user base (25%) used the recommender-switching feature. The majority of users who used the control only switched algorithms a few times, trying a few out and settling down on an algorithm that they would leave alone. The largest number of users prefer a matrix factorization algorithm, followed closely by item-item collaborative filtering; users selected both of these algorithms much more often than they chose a non-personalized mean recommender. The algorithms did produce measurably different recommender lists for the users in the study, but these differences were not directly predictive of user choice.

links and other notes:

• http://dl.acm.org/authorize?N03674
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/JIIKRQXF

F. Maxwell Harper, Funing Xu, Harmanpreet Kaur, Kyle Condiff, Shuo Chang, and Loren Terveen. 2015. Putting Users in Control of Their Recommendations. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15), 3–10. https://doi.org/10.1145/2792838.2800179

abstract:

The essence of a recommender system is that it can recommend items personalized to the preferences of an individual user. But typically users are given no explicit control over this personalization, and are instead left guessing about how their actions affect the resulting recommendations. We hypothesize that any recommender algorithm will better fit some users' expectations than others, leaving opportunities for improvement. To address this challenge, we study a recommender that puts some control in the hands of users. Specifically, we build and evaluate a system that incorporates user-tuned popularity and recency modifiers, allowing users to express concepts like "show more popular items". We find that users who are given these controls evaluate the resulting recommendations much more positively. Further, we find that users diverge in their preferred settings, confirming the importance of giving control to users.

links and other notes:

• author version: http://files.grouplens.org/papers/harper-recsys2015.pdf
• http://doi.acm.org/10.1145/2792838.2800179
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FZ2K4HP6

Komal Kapoor, Vikas Kumar, Loren Terveen, Joseph A. Konstan, and Paul Schrater. 2015. “I Like to Explore Sometimes”: Adapting to Dynamic User Novelty Preferences. In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys ’15), 19–26. https://doi.org/10.1145/2792838.2800172

abstract:

Studies have shown that the recommendation of unseen, novel or serendipitous items is crucial for a satisfying and engaging user experience. As a result, recent developments in recommendation research have increasingly focused towards introducing novelty in user recommendation lists. While, existing solutions aim to find the right balance between the similarity and novelty of the recommended items, they largely ignore the user needs for novelty. In this paper, we show that there are large individual and temporal differences in the users' novelty preferences. We develop a regression model to predict these dynamic novelty preferences of users using features derived from their past interactions. Finally, we describe an adaptive recommender,~\emph{adaNov-R}, that adapts to the user needs for novel items and show that the model achieves better recommendation performance on a metric that considers both novel and familiar items.

links and other notes:

• http://dl.acm.org/authorize?N03965
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/G9VI7F5Q

Shilad Sen, Isaac Johnson, Rebecca Harper, Huy Mai, Samuel Horlbeck Olsen, Benjamin Mathers, Laura Souza Vonessen, Matthew Wright, and Brent Hecht. 2015. Towards domain-specific semantic relatedness: a case study from geography. In Proceedings of the 24th International Conference on Artificial Intelligence, 2362–2370. Retrieved May 4, 2016 from http://ijcai.org/papers15/Papers/IJCAI15-334.pdf

abstract:

Semantic relatedness (SR) measures form the algorithmic foundation of intelligent technologies in domains ranging from artificial intelligence to human-computer interaction. Although SR has been researched for decades, this work has focused on developing general SR measures rooted in graph and text mining algorithms that perform reasonably well for many different types of concepts. This paper introduces domain-specific SR, which augments general SR by identifying, capturing, and synthesizing domain-specific relationships between concepts. Using the domain of geography as a case study, we show that domain-specific SR -- and even geography-specific signals alone (e.g. distance, containment) without sophisticated graph or text mining algorithms -- significantly outperform the SR state-of-the-art for geographic concepts. In addition to substantially improving SR measures for geospatial technologies, an area that is rapidly increasing in importance, this work also unlocks an important new direction for SR research: SR measures that incorporate domain-specific customizations to increase accuracy.

links and other notes:

• http://ijcai.org/papers15/Papers/IJCAI15-334.pdf
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3DPVTWUJ

Guo Li, Haiyi Zhu, Tun Lu, Xianghua Ding, and Ning Gu. 2015. Is It Good to Be Like Wikipedia?: Exploring the Trade-offs of Introducing Collaborative Editing Model to Q&A Sites. In 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 1080–1091. https://doi.org/10.1145/2675133.2675155

abstract:

Online question and answer (Q&A) sites, which are platforms for users to post and answer questions on a wide range of topics, are becoming large repositories of valuable knowledge and important to societies. In order to sustain success, Q&A sites face the challenges of ensuring content quality and encouraging user contributions. This paper examines a particular design decision in Q&A sites-allowing Wikipedia-like collaborative editing on questions and answers, and explores its beneficial effects on content quality and potential detrimental effects on users' contributions. By examining five years' archival data of Stack Overflow, we found that the benefits of collaborative editing outweigh its risks. For example, each substantive edit from other users can increase the number of positive votes by 181% for the questions and 119% for the answers. On the other hand, each edit only decreases askers and answerers' subsequent contributions by no more than 5%. This work has implications for understanding and designing large-scale social computing systems.

links and other notes:

• http://dl.acm.org/authorize?N00247
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/MRDAM4A5

Shilad W. Sen, Heather Ford, David R. Musicant, Mark Graham, Oliver S.B. Keyes, and Brent Hecht. 2015. Barriers to the Localness of Volunteered Geographic Information. In 33rd Annual ACM Conference on Human Factors in Computing Systems, 197–206. https://doi.org/10.1145/2702123.2702170

abstract:

Localness is an oft-cited benefit of volunteered geographic information (VGI). This study examines whether localness is a constant, universally shared benefit of VGI, or one that varies depending on the context in which it is produced. Focusing on articles about geographic entities (e.g. cities, points of interest) in 79 language editions of Wikipedia, we examine the localness of both the editors working on articles and the sources of the information they cite. We find extensive geographic inequalities in localness, with the degree of localness varying with the socioeconomic status of the local population and the health of the local media. We also point out the key role of language, showing that information in languages not native to a place tends to be produced and sourced by non-locals. We discuss the implications of this work for our understanding of the nature of VGI and highlight a generalizable technical contribution: an algorithm that determines the home country of the original publisher of online content.

links and other notes:

• http://dl.acm.org/authorize?N00090
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ECEUX3IT

Pavel Andreevich Samsonov, Xun Tang, Johannes Schöning, Werner Kuhn, and Brent Hecht. 2015. You Can’t Smoke Here: Towards Support for Space Usage Rules in Location-aware Technologies. In 33rd Annual ACM Conference on Human Factors in Computing Systems, 971–974. https://doi.org/10.1145/2702123.2702269

abstract:

Recent work has identified the lack of space usage rule (SUR) data -- e.g. "no smoking", "no campfires" -- as an important limitation of online/mobile maps that presents risks to user safety and the environment. In order to address this limitation, a large-scale means of mapping SURs must be developed. In this paper, we introduce and motivate the problem of mapping space usage rules and take the first steps towards identifying solutions. We show how computer vision can be employed to identify SUR indicators in the environment (e.g. "No Smoking" signs) with reasonable accuracy and describe techniques that can assign each rule to the appropriate geographic feature.

links and other notes:

• http://dl.acm.org/authorize?N00099
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CFTC6J64

Pavel Andreevich Samsonov, Johannes Schöning, and Brent Hecht. 2015. A User Interface for Encoding Space Usage Rules Expressed in Natural Language. In 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, 2211–2216. https://doi.org/10.1145/2702613.2732737

abstract:

Our interactions with the spaces around us are frequently defined by space usage rules (SURs) like "no smoking", "no dogs allowed", and "stay on the trail". These rules are important public health tools and help protect the environment, among other applications. However, despite their importance, no large-scale database of SURs currently exists. This prevents online/mobile maps from presenting these rules to users, as their traditional paper counterparts have been shown to do regularly. The lack of a SUR database also prevents developers from building novel SUR-based applications, e.g. mobile apps that inform users where they can smoke or walk their dog. In this paper, we present an in-development user interface to support the semi-automatic encoding of SURs expressed in natural language in documents such as laws, city ordinances, park rules, and institution FAQ pages. Our system will allow an untrained user to easily enter complex rules into a spatial data format compatible with OpenStreetMap.

links and other notes:

• http://dl.acm.org/authorize?N00098
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NMQ3MH2P

Morten Warncke-Wang, Vladislav R. Ayukaev, Brent Hecht, and Loren G. Terveen. 2015. The Success and Failure of Quality Improvement Projects in Peer Production Communities. In 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 743–756. https://doi.org/10.1145/2675133.2675241

abstract:

Peer production communities have been proven to be successful at creating valuable artefacts, with Wikipedia as a prime example. However, a number of studies have shown that work in these communities tends to be of uneven quality and certain content areas receive more attention than others. In this paper, we examine the efficacy of a range of targeted strategies to increase the quality of under-attended content areas in peer production communities. Mining data from five quality improvement projects in the English Wikipedia, the largest peer production community in the world, we show that certain types of strategies (e.g. creating artefacts from scratch) have better quality outcomes than others (e.g. improving existing artefacts), even if both are done by a similar cohort of participants. We discuss the implications of our findings for Wikipedia as well as other peer production communities.

links and other notes:

• http://dl.acm.org/authorize?N00097
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2VHT8QWW

Shilad Sen, Margaret E. Giesel, Rebecca Gold, Benjamin Hillmann, Matt Lesicko, Samuel Naden, Jesse Russell, Zixiao (Ken) Wang, and Brent Hecht. 2015. Turkers, Scholars, “Arafat” and “Peace”: Cultural Communities and Algorithmic Gold Standards. In 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, 826–838. https://doi.org/10.1145/2675133.2675285

abstract:

In just a few years, crowdsourcing markets like Mechanical Turk have become the dominant mechanism for for building "gold standard" datasets in areas of computer science ranging from natural language processing to audio transcription. The assumption behind this sea change - an assumption that is central to the approaches taken in hundreds of research projects - is that crowdsourced markets can accurately replicate the judgments of the general population for knowledge-oriented tasks. Focusing on the important domain of semantic relatedness algorithms and leveraging Clark's theory of common ground as a framework, we demonstrate that this assumption can be highly problematic. Using 7,921 semantic relatedness judgements from 72 scholars and 39 crowdworkers, we show that crowdworkers on Mechanical Turk produce significantly different semantic relatedness gold standard judgements than people from other communities. We also show that algorithms that perform well against Mechanical Turk gold standard datasets do significantly worse when evaluated against other communities' gold standards. Our results call into question the broad use of Mechanical Turk for the development of gold standard datasets and demonstrate the importance of understanding these datasets from a human-centered point-of-view. More generally, our findings problematize the notion that a universal gold standard dataset exists for all knowledge tasks.

links and other notes:

• http://dl.acm.org/authorize?N00096
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3KZSZXC8

Dirk Wenig, Johannes Schöning, Brent Hecht, and Rainer Malaka. 2015. StripeMaps: Improving Map-based Pedestrian Navigation for Smartwatches. In 17th International Conference on Human-Computer Interaction with Mobile Devices and Services, 52–62. https://doi.org/10.1145/2785830.2785862

abstract:

Map applications for smartwatches present new challenges in cartography, a domain in which large display sizes have significant advantages. In this paper, we introduce StripeMaps, a system that adapts the mobile web design technique of linearization for displaying maps on the small screens of smartwatches. Just as web designers simplify multiple column desktop websites into a single column for easier navigation on mobile devices, StripeMaps transforms any two-dimensional route map into a one-dimensional "stripe". Through a user study, we show that this simplification allows StripeMaps to outperform both traditional mobile map interfaces and turn-by-turn directions for pedestrian navigation using smartwatches. In addition to introducing StripeMaps, this paper also has a secondary contribution. It contains the first empirical comparison of different approaches for pedestrian smartwatch navigation and illuminates their pros and cons.

links and other notes:

• http://dl.acm.org/authorize?N00095
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/JV2IA3UG

Joseph A. Konstan and Jack W. Davidson. 2015. Who Pays When Autonomous Fails? Response. ASSOC COMPUTING MACHINERY 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA. Retrieved April 6, 2016 from http://scholar.google.com/scholar?cluster=1157554090278713461&hl=en&oi=scholarr

abstract:

links and other notes:

• http://scholar.google.com/scholar?cluster=1157554090278713461&hl=en&oi=scholarr
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/SEJJJE48

Hannah J. Miller, Shuo Chang, and Loren G. Terveen. 2015. “I LOVE THIS SITE!” vs. “It’s a Little Girly”: Perceptions of and Initial User Experience with Pinterest. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’15), 1728–1740. https://doi.org/10.1145/2675133.2675269

abstract:

Pinterest is a popular social networking site that lets people discover, collect, and share pictures of items from the Web. Among popular social media sites, Pinterest has by far the most skewed gender distribution: women are four times more likely than men to use it. To better understand this, we examined two factors that generally affect whether people try a social site and whether they continue using it: the external perception of a site (e.g., as conveyed in popular media) and the site's initial user experience. For the latter, we focused on the role of social bootstrapping, importing contacts from one social site to another. We conducted a survey study, finding that: perceptions of Pinterest among users and non-users of the site differed significantly; trying Pinterest led to substantial changes in user perceptions of the site; social bootstrapping affected users' initial impression of Pinterest, generally improving it for women and harming it for men. We present implications of our findings for design and research.

links and other notes:

• http://dl.acm.org/authorize?N97455
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ZDR8VXA2

Jacob Thebault-Spieker, Loren G. Terveen, and Brent Hecht. 2015. Avoiding the South Side and the Suburbs: The Geography of Mobile Crowdsourcing Markets. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’15), 265–275. https://doi.org/10.1145/2675133.2675278

abstract:

Mobile crowdsourcing markets (e.g., Gigwalk and TaskRabbit) offer crowdworkers tasks situated in the physical world (e.g., checking street signs, running household errands). The geographic nature of these tasks distinguishes these markets from online crowdsourcing markets and raises new, fundamental questions. We carried out a controlled study in the Chicago metropolitan area aimed at addressing two key questions: (1) What geographic factors influence whether a crowdworker will be willing to do a task? (2) What geographic factors influence how much compensation a crowdworker will demand in order to do a task? Quantitative modeling shows that travel distance to the location of the task and the socioeconomic status (SES) of the task area are important factors. Qualitative analysis enriches our modeling, with workers mentioning safety and difficulties getting to a location as key considerations. Our results suggest that low-SES areas are currently less able to take advantage of the benefits of mobile crowdsourcing markets. We discuss the implications of our study for these markets, as well as for "sharing economy" phenomena like UberX, which have many properties in common with mobile crowdsourcing markets.

links and other notes:

• http://dl.acm.org/authorize?N97344
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CPT8NP8F

Thomas Stock, Brent Hecht, and Johannes Schöning. 2014. SubwayPS: Towards Enabling Smartphone Positioning in Underground Public Transportation Systems. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL 14), 93–102. Retrieved from http://dl.acm.org/authorize?N95928

abstract:

Thanks to rapid advances in technologies like GPS and Wi-Fi positioning, smartphone users are able to determine their location almost everywhere they go. This is not true, however, of people who are traveling in underground public transportation networks, one of the few types of high-traffic areas where smartphones do not have access to accurate position information. In this paper, we introduce the problem of underground transport positioning on smartphones and present SubwayPS, an accelerometer-based positioning technique that allows smartphones to determine their location substantially better than baseline approaches, even deep beneath city streets. We highlight several immediate applications of positioning in subway networks in domains ranging from mobile advertising to mobile maps and present MetroNavigator, a proof-of-concept smartphone and smartwatch app that notifies users of upcoming points-of-interest and alerts them when it is time to get ready to exit the train.

links and other notes:

• http://dl.acm.org/authorize?N95928
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/542V6VNN

Vikas Kumar, Daniel Kluver, Loren Terveen, and John Riedl. 2014. More Efficient Tagging Systems with Tag Seeding. Academy of Science and Engineering (ASE), USA, ©ASE 2014. Retrieved February 13, 2015 from http://www-users.cs.umn.edu/~vikas/papers/TagSeeding_SocialCom_Vikas.pdf

abstract:

Tags are a useful mechanism for users to find, organize, and understand items on a web site. A tagging system evolves to reflect a user community's understanding of an information space. However, the evolution can be problematic: specifically, the ratio of tags to items can decrease over time, to the extent that tags no longer effectively discriminate among items. The primary reason being over application of existing popular tags to most of the existing or new items thus making it difficult for users to search and navigate efficiently using tags. We address this problem of disproportionate application of existing popular tags by eliciting applications of those tags that are under applied but highly relevant. In this paper, we introduce two metrics to identify such under applied relevant tags from the system. We then design a controlled study to elicit more applications of these tags, to show how the declining discriminating power of tags is counteracted by interventions in tag applications using these carefully chosen under applied tags, namely seed tags. We found that users were able to apply these underutilized tags, with the result that about eleven months of lost navigational and search efficiency of the system was attained back. We also monitored subsequent usage of these tags in the system and found that the seeded tags do attract more users in navigation with high visibility and more applications than before our intervention, suggesting tag seeding has a persistent effect over the system.

links and other notes:

• http://www-users.cs.umn.edu/~vikas/papers/TagSeeding_SocialCom_Vikas.pdf
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/G5AGD6UN

Brent Hecht and Monica Stephens. 2014. A Tale of Cities: Urban Biases in Volunteered Geographic Information. In Eighth International AAAI Conference on Weblogs and Social Media. Retrieved February 13, 2015 from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8114

abstract:

Geotagged tweets, Foursquare check-ins and other forms of volunteered geographic information (VGI) play a critical role in numerous studies and a large range of intelligent technologies. We show that three of the most commonly used sources of VGI – Twitter, Flickr, and Foursquare – are biased towards urban perspectives at the expense of rural ones. Utilizing a geostatistics-based approach, we demonstrate that, on a per capita basis, these important VGI datasets have more users, more information, and higher quality information within metropolitan areas than outside of them. VGI is a subset of user-generated content (UGC) and we discuss how our results suggest that urban biases might exist in non-geographically referenced UGC as well. Finally, because Foursquare is exclusively made up of VGI, we argue that Foursquare (and possibly other location-based social networks) has fundamentally failed to appeal to rural populations.

links and other notes:

• http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8114
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/DSWDCS55

Kate Grandprey-Shores, Yilin He, Kristina L. Swanenburg, Robert Kraut, and John Riedl. 2014. The Identification of Deviance and Its Impact on Retention in a Multiplayer Game. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’14), 1356–1365. https://doi.org/10.1145/2531602.2531724

abstract:

Deviant behavior in online social systems is a difficult problem to address. Consequences of deviance include driving off users and tarnishing the system's public image. We present an examination of these concepts in a popular online game, League of Legends. Using a large collection of game records and player-given feedback, we develop a metric, toxicity index, to identify deviant players. We then look at the effects of interacting with deviant players, including effects on retention. We find that toxic players have several significant predictive patterns, such as playing in more competitive game modes and playing with friends. We also show that toxic players drive away new players, but that experienced players are more resilient to deviant behavior. Based on our findings, we suggest methods to better identify and counteract the negative effects of deviance.

links and other notes:

• http://dl.acm.org/authorize?N95129
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ID7D69V7

Mikhil Masli and Loren G. Terveen. 2014. Leveraging the Contributory Potential of User Feedback. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’14), 956–966. https://doi.org/10.1145/2531602.2531686

abstract:

Under contribution is an important problem in online social production communities: important tasks don't get done, and only a small minority of participants are active contributors. How can we remedy this situation? We explore the feasibility of using the act of consuming information as a gateway to contributing information; specifically, we investigate semi-automated means to extract useful information from standard types of user feedback. We explore this approach in the context of a geographic wiki and route-planning system for bicyclists. We analyzed naturally occurring textual route feedback, finding that the feedback was rich in information such as bikeability ratings, tags and notes that are useful to improve the system's route finding and navigational assistance capabilities. We also present a technique to extract such information by engaging users in dialogue immediately after they obtain a route. We believe that our results and ideas are applicable to a broad class of social production systems.

links and other notes:

• http://dl.acm.org/authorize?N95121
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/8CPFJGKM

Aaron Halfaker, R. Stuart Geiger, and Loren G. Terveen. 2014. Snuggle: Designing for Efficient Socialization and Ideological Critique. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), 311–320. https://doi.org/10.1145/2556288.2557313

abstract:

Wikipedia, the encyclopedia "anyone can edit", has become increasingly less so. Recent academic research and popular discourse illustrates the often aggressive ways newcomers are treated by veteran Wikipedians. These are complex sociotechnical issues, bound up in infrastructures based on problematic ideologies. In response, we worked with a coalition of Wikipedians to design, develop, and deploy Snuggle, a new user interface that served two critical functions: making the work of newcomer socialization more effective, and bringing visibility to instances in which Wikipedians? current practice of gatekeeping socialization breaks down. Snuggle supports positive socialization by helping mentors quickly find newcomers whose good-faith mistakes were reverted as damage. Snuggle also supports ideological critique and reflection by bringing visibility to the consequences of viewing newcomers through a lens of suspiciousness.

links and other notes:

• http://dl.acm.org/authorize?N95127
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/79T6JNIJ

Daniel Kluver and Joseph A. Konstan. 2014. Evaluating Recommender Behavior for New Users. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14), 121–128. https://doi.org/10.1145/2645710.2645742

abstract:

The new user experience is one of the important problems in recommender systems. Past work on recommending for new users has focused on the process of gathering information from the user. Our work focuses on how different algorithms behave for new users. We describe a methodology that we use to compare representatives of three common families of algorithms along eleven different metrics. We find that for the first few ratings a baseline algorithm performs better than three common collaborative filtering algorithms. Once we have a few ratings, we find that Funk's SVD algorithm has the best overall performance. We also find that ItemItem, a very commonly deployed algorithm, performs very poorly for new users. Our results can inform the design of interfaces and algorithms for new users.

links and other notes:

• http://dl.acm.org/authorize?N84042
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/9R3MJGKP

Ting-Yu Wang, F. Maxwell Harper, and Brent Hecht. 2014. Designing Better Location Fields in User Profiles. In Proceedings of the 18th International Conference on Supporting Group Work (GROUP ’14), 73–80. https://doi.org/10.1145/2660398.2660424

abstract:

Twitter, Facebook, Pinterest and many other online communities ask their users to populate a location field in their user profiles. The information that is entered into this field has many uses in both industry and academia, with location field data providing valuable geographic context for operators of online communities and playing key roles in numerous research projects. However, despite the importance of location field entries, we know little about how to design location fields effectively. In this paper, we report the results of the first controlled study of the design of location fields in user profiles. After presenting a survey of location field design decisions in use across many online communities, we show that certain design decisions can lead to more granular location information or a higher percentage of users that fill out the field, but that there is a trade-off between granularity and the percent of non-empty fields. We also add context to previous work that found that location fields tend to have a high rate of non-geographic information (e.g. Location: "Justin Bieber's Heart"), showing that this result may be site-specific rather than endemic to all location fields. Finally, we provide evidence that verifying users' location field entries against a database of known-valid locations can eliminate toponym (place name) ambiguity and any non-geographic location field entries while at the same time having little effect on field population rate and granularity.

links and other notes:

• http://doi.acm.org/10.1145/2660398.2660424
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/6FPFA8NG

Loren Terveen, Joseph A. Konstan, and Cliff Lampe. 2014. Study, Build, Repeat: Using Online Communities as a Research Platform. In Ways of Knowing in HCI, Judith S. Olson and Wendy A. Kellogg (eds.). Springer New York, 95–117. Retrieved March 20, 2015 from http://link.springer.com/chapter/10.1007/978-1-4939-0378-8_5

abstract:

Research on online communities raises a number of challenges. It is difficult to get access to usage data, to users (to interview), and to the system itself to introduce new features (e.g., participation incentive mechanisms). One solution is for researchers to create an online community themselves. Although this provides more control and access, it also requires additional resources (e.g., for staff to maintain the community) and consideration of the needs of the user community after the research is completed.

links and other notes:

• http://link.springer.com/chapter/10.1007/978-1-4939-0378-8_5
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CTAVISTH

Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. In Proceedings of the 23rd International Conference on World Wide Web (WWW ’14), 677–686. https://doi.org/10.1145/2566486.2568012

abstract:

Eli Pariser coined the term 'filter bubble' to describe the potential for online personalization to effectively isolate people from a diversity of viewpoints or content. Online recommender systems - built on algorithms that attempt to predict which items users will most enjoy consuming - are one family of technologies that potentially suffers from this effect. Because recommender systems have become so prevalent, it is important to investigate their impact on users in these terms. This paper examines the longitudinal impacts of a collaborative filtering-based recommender system on users. To the best of our knowledge, it is the first paper to measure the filter bubble effect in terms of content diversity at the individual level. We contribute a novel metric to measure content diversity based on information encoded in user-generated tags, and we present a new set of methods to examine the temporal effect of recommender systems on the user experience. We do find that recommender systems expose users to a slightly narrowing set of items over time. However, we also see evidence that users who actually consume the items recommended to them experience lessened narrowing effects and rate items more positively.

links and other notes:

• http://dl.acm.org/authorize?N03685
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/XP6STJMI

Michael D. Ekstrand, F. Maxwell Harper, Martijn C. Willemsen, and Joseph A. Konstan. 2014. User Perception of Differences in Recommender Algorithms. In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys ’14), 161–168. https://doi.org/10.1145/2645710.2645737

abstract:

Recent developments in user evaluation of recommender systems have brought forth powerful new tools for understanding what makes recommendations effective and useful. We apply these methods to understand how users evaluate recommendation lists for the purpose of selecting an algorithm for finding movies. This paper reports on an experiment in which we asked users to compare lists produced by three common collaborative filtering algorithms on the dimensions of novelty, diversity, accuracy, satisfaction, and degree of personalization, and to select a recommender that they would like to use in the future. We find that satisfaction is negatively dependent on novelty and positively dependent on diversity in this setting, and that satisfaction predicts the user's final selection. We also compare users' subjective perceptions of recommendation properties with objective measures of those same characteristics. To our knowledge, this is the first study that applies modern survey design and analysis techniques to a within-subjects, direct comparison study of recommender algorithms.

links and other notes:

• http://dl.acm.org/authorize?N83933
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/82T48QXH

Katherine Panciera, Mikhil Masli, and Loren Terveen. 2014. Cream of the crop: Elite contributors in an online community. In The International Symposium on Open Collaboration, 1–10. https://doi.org/10.1145/2641580.2641609

abstract:

In open content communities like Wikipedia and StackOverflow and in open source software projects, a small proportion of users produce a majority of the content and take on much of the required community maintenance work. Understanding this class of users is crucial to creating and sustaining healthy communities. We carried out a mixed-method study of core contributors to the Cyclopath geographic wiki and bicycle routing web site. We present our findings and organize our discussion using concepts from activity theory. We found that the Cyclopath core contributors aren't the dedicated cyclists and that the characteristics of the community shape the site, the rules, and the tools for contributing. Additionally, we found that numerous aspects about the surrounding ecology of related systems and communities may help to shape how the site functions and views itself. We draw implications for future research and design from these findings.

links and other notes:

• http://dl.acm.org/authorize?N00017
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NTWBJEKT

Brent Hecht and David A. Shamma. 2014. A crash course in modern geography for HCI researchers and practitioners. In CHI ’14 Extended Abstracts on Human Factors in Computing Systems, 1031–1032. https://doi.org/10.1145/2559206.2567824

abstract:

Recently, geography's role in human-computer interaction has risen due in part to the popularity of social computing as well as the number of ubiquitous GPS enabled devices. We now, either explicitly or implicitly, track, store capture, and annotate our surroundings constantly through out the day. In turn this changes how one might come to understand and perceive the spaces and locations around us. This course builds a framework for researchers and practitioners in geographic human-computer interaction, providing an introduction to foundational literature, modern geography, as well as, the qualitative and quantitative research practices that are most relevant to the HCI community.

links and other notes:

• http://dl.acm.org/authorize?N00005
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ENJ9W6PD

Toby Jia-Jun Li, Shilad Sen, and Brent Hecht. 2014. Leveraging advances in natural language processing to better understand Tobler’s first law of geography. In 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 513–516. https://doi.org/10.1145/2666310.2666493

abstract:

Tobler's First Law of Geography (TFL) is one of the key reasons why "spatial is special". The law, which states that "everything is related to everything else, but near things are more related than distant things", is central to the management, presentation, and analysis of geographic information. However, despite the importance of TFL, we have a limited general understanding of its domain-neutral properties. In this paper, we leverage recent advances in the natural language processing domain of semantic relatedness estimation to, for the first time, robustly evaluate the extent to which relatedness between spatial entities decreases over distance in a domain-neutral fashion. Our results reveal that, in general, TFL can indeed be considered a globally recognized domain-neutral property of geographic information but that there is a distance beyond which being nearer, on average, no longer means being more related.

links and other notes:

• http://dl.acm.org/authorize?N00094
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3SZNUBV7

Johannes Schöning, Brent Hecht, and Werner Kuhn. 2014. Informing Online and Mobile Map Design with the Collective Wisdom of Cartographers. In Proceedings of the 2014 Conference on Designing Interactive Systems (DIS ’14), 765–774. https://doi.org/10.1145/2598510.2598543

abstract:

Despite the large and growing prominence of online and mobile maps, they have not been broadly and systematically examined with a lens informed by traditional cartography. Using an approach rooted in cartographic theory and a unique dataset of 382 publicly-displayed local maps, we identify the collective wisdom of hundreds of cartographers with respect to a number of cartographic design decisions. We compare our findings to the approaches taken in popular online and mobile map platforms and develop suggestions for incorporating the collective wisdom of cartographers into these systems. Our suggestions include the adoption of location-aware cartography, in which cartographic approaches are intelligently varied based on the type of location being viewed. We provide mockup designs of online and mobile maps that implement our suggestions and discuss means by which the surprising gap between online and mobile maps and traditional cartography may be bridged.

links and other notes:

• http://dl.acm.org/authorize?N97483
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ENDMHET5

Shilad Sen, Toby Jia-Jun Li, WikiBrain Team, and Brent Hecht. 2014. WikiBrain: Democratizing Computation on Wikipedia. In Proceedings of The International Symposium on Open Collaboration (OpenSym ’14), 27:1-27:10. https://doi.org/10.1145/2641580.2641615

abstract:

Wikipedia is known for serving humans' informational needs. Over the past decade, the encyclopedic knowledge encoded in Wikipedia has also powerfully served computer systems. Leading algorithms in artificial intelligence, natural language processing, data mining, geographic information science, and many other fields analyze the text and structure of articles to build computational models of the world. Many software packages extract knowledge from Wikipedia. However, existing tools either (1) provide Wikipedia data, but not well-known Wikipedia-based algorithms or (2) narrowly focus on one such algorithm. This paper presents the WikiBrain software framework, an extensible Java-based platform that democratizes access to a range of Wikipedia-based algorithms and technologies. WikiBrain provides simple access to the diverse Wikipedia data needed for semantic algorithms and technologies, ranging from page views to Wikidata. In a few lines of code, a developer can use WikiBrain to access Wikipedia data and state-of-the-art algorithms. WikiBrain also enables researchers to extend Wikipedia-based algorithms and evaluate their extensions. WikiBrain promotes a new vision of the Wikipedia software ecosystem: every researcher and developer should have access to state-of-the-art Wikipedia-based technologies.

links and other notes:

• http://dl.acm.org/authorize?N97482
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/XGMWKF73

Joseph A. Konstan, J.D. Walker, D. Christopher Brooks, Keith Brown, and Michael D. Ekstrand. 2014. Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. In Proceedings of the First ACM Conference on Learning @ Scale Conference (L@S ’14), 61–70. https://doi.org/10.1145/2556325.2566244

abstract:

In Fall 2013 we offered an open online Introduction to Recommender Systems through Coursera, while simultaneously offering a for-credit version of the course on-campus using the Coursera platform and a flipped classroom instruction model. As the goal of offering this course was to experiment with this type of instruction, we performed extensive evaluation including surveys of demographics, self-assessed skills, and learning intent; we also designed a knowledge-assessment tool specifically for the subject matter in this course, administering it before and after the course to measure learning. We also tracked students through the course, including separating out students enrolled for credit from those enrolled only for the free, open course. This article reports on our findings.

links and other notes:

• http://dl.acm.org/authorize?N89601
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/VC5WCWZR

Anne Oeldorf-Hirsch, Brent Hecht, Meredith Ringel Morris, Jaime Teevan, and Darren Gergle. 2014. To Search or to Ask: The Routing of Information Needs Between Traditional Search Engines and Social Networks. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’14), 16–27. https://doi.org/10.1145/2531602.2531706

abstract:

In status message question asking (SMQA), members of social networking sites make use of status messages to express information needs to friends and contacts. We present findings from a laboratory study that examined 82 participants' SMQA behaviors in the broader context of online information seeking. When given the option of using a search engine and/or a social network, participants leveraged SMQA for 20% of their information needs, most often posing a question to their network in addition to issuing a query. We show the important roles played by the specificity of the information need and the perceived audience of a given network on routing decisions. We then demonstrate that routing decisions have varied effects on participants' satisfaction, information value, and trust of outcomes. In addition to highlighting the complementary advantages and disadvantages of search and SMQA, our findings suggest that search engines can better address a meaningful portion of people's information needs by integrating SMQA capabilities into their systems.

links and other notes:

• http://dl.acm.org/authorize?N95120
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/45SZ5PXK

S. Andrew Sheppard, Andrea Wiggins, and Loren Terveen. 2014. Capturing Quality: Retaining Provenance for Curated Volunteer Monitoring Data. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’14), 1234–1245. https://doi.org/10.1145/2531602.2531689

abstract:

The "real world" nature of field-based citizen science involves unique data management challenges that distinguish it from projects that involve only Internet-mediated activities. In particular, many data contribution and review practices are often accomplished "offline' via paper or general-purpose software like Excel. This can lead to integration challenges when attempting to implement project-specific ICT with full revision and provenance tracking. In this work, we explore some of the current challenges and opportunities in implementing ICT for managing volunteer monitoring data. Our two main contributions are: a general outline of the workflow tasks common to field-based data collection, and a novel data model for preserving provenance metadata that allows for ongoing data exchange between disparate technical systems and participant skill levels. We conclude with applications for other domains, such as hydrologic forecasting and crisis informatics, as well as directions for future research.

links and other notes:

• http://dl.acm.org/authorize?N95128
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/VAUVFDWN

Catherine Grevet, Loren G. Terveen, and Eric Gilbert. 2014. Managing Political Differences in Social Media. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’14), 1400–1408. https://doi.org/10.1145/2531602.2531676

abstract:

Most people associate with people like themselves, a process called homophily. Exposure to diversity, however, makes us more informed as individuals and as a society. In this paper, we investigate political disagreements on Facebook to explore the conditions under which diverse opinions can coexist online. Via a mixed methods approach comprising 103 survey responses and 13 interviews with politically engaged American social media users, we found that participants who perceived more differences with their friends engaged less on Facebook than those who perceived more homogeneity. Weak ties were particularly brittle to political disagreements, despite being the ties most likely to offer diversity. Finally, based on our findings we suggest potential design opportunities to bridge across ideological difference: 1) support exposure to weak ties; and 2) make common ground visible while friends converse.

links and other notes:

• http://dl.acm.org/authorize?N95122
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/XQFJ8SWB

Tong Gao, Jessica R. Hullman, Eytan Adar, Brent Hecht, and Nicholas Diakopoulos. 2014. NewsViews: An Automated Pipeline for Creating Custom Geovisualizations for News. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’14), 3005–3014. https://doi.org/10.1145/2556288.2557228

abstract:

Interactive visualizations add rich, data-based context to online news articles. Geographic maps are currently the most prevalent form of these visualizations. Unfortunately, designers capable of producing high-quality, customized geovisualizations are scarce. We present NewsViews, a novel automated news visualization system that generates interactive, annotated maps without requiring professional designers. NewsViews' maps support trend identification and data comparisons relevant to a given news article. The NewsViews system leverages text mining to identify key concepts and locations discussed in articles (as well as potential annotations), an extensive repository of 'found' databases, and techniques adapted from cartography to identify and create visually 'interesting' thematic maps. In this work, we develop and evaluate key criteria in automatic, annotated, map generation and experimentally validate the key features for successful representations (e.g., relevance to context, variable selection, 'interestingness' of representation and annotation quality).

links and other notes:

• http://dl.acm.org/authorize?N95126
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NHFC553U

Shuo Chang, Vikas Kumar, Eric Gilbert, and Loren G. Terveen. 2014. Specialization, Homophily, and Gender in a Social Curation Site: Findings from Pinterest. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW ’14), 674–686. https://doi.org/10.1145/2531602.2531660

abstract:

Pinterest is a popular social curation site where people collect, organize, and share pictures of items. We studied a fundamental issue for such sites: what patterns of activity attract attention (audience and content reposting)-- We organized our studies around two key factors: the extent to which users specialize in particular topics, and homophily among users. We also considered the existence of differences between female and male users. We found: (a) women and men differed in the types of content they collected and the degree to which they specialized; male Pinterest users were not particularly interested in stereotypically male topics; (b) sharing diverse types of content increases your following, but only up to a certain point; (c) homophily drives repinning: people repin content from other users who share their interests; homophily also affects following, but to a lesser extent. Our findings suggest strategies both for users (e.g., strategies to attract an audience) and maintainers (e.g., content recommendation methods) of social curation sites.

links and other notes:

• http://dl.acm.org/authorize?N95123
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/6HQ3RBT9

Fernando Torre, Yanjie Liu, Zhengjie Liu, and Loren Terveen. 2013. Local Knowledge Matters for Crowdsourcing Systems: Experience from Transferring an American Site to China. In Seventh International AAAI Conference on Weblogs and Social Media. Retrieved April 9, 2015 from https://grouplens.org/site-content/uploads/2013/09/6108-30344-1-PB.pdf

abstract:

We present the results of a preliminary cross-cultural study of Cyclopath, a geographic crowdsourcing system for cyclists. We created a Chinese version and tested it in lab with 40 cyclists in Dalian, China. We analyzed the data entered by users and found some significant differences compared to similar studies done in the US; this illustrates the need for local knowledge in crowdsourcing applications. From usage patterns and interview data, we observed that landmarks play a larger role for navigating the map and requesting routes; this suggests that integrating landmarks better into the Cyclopath UI could improve Chinese users’ experience with the system. Finally, we found that concepts of authority and hierarchy may influence Chinese users’ trust of peer-contributed content; this suggests that reputation mechanisms may help improve trust.

links and other notes:

• http://grouplens.org/site-content/uploads/2013/09/6108-30344-1-PB.pdf
• https://grouplens.org/site-content/uploads/2013/09/6108-30344-1-PB.pdf
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/S8HIB6JX

M. Sarwat, A. Eldawy, M.F. Mokbel, and J. Riedl. 2013. PLUTUS: Leveraging Location-Based Social Networks to Recommend Potential Customers to Venues. In 2013 IEEE 14th International Conference on Mobile Data Management (MDM), 26–35. https://doi.org/10.1109/MDM.2013.13

abstract:

In a business setting, the customer value is crucial as it determines how much it is worth spending to acquire a particular customer. Viral marketing techniques leverages social ties among users to help advertising a particular product. Recently, as mobile devices (e.g., smart phones, GPS devices) became ubiquitous, location-based social networking websites (e.g., Gowalla, BrightKite, Foursquare) are getting more and more popular. Along with location-based social networking services being prominent, new kind of data came into play besides the traditional social networking data: (1) Spatial data: represents the users geo-locations, venues geo-locations and information about users visiting different venues. (2) Users Opinions data: represents how much a user likes the venues she visits (e.g., Alice visited restaurant A and gave it a rating of five over five). In this paper, we present PLUTUS; a framework that assists venues (e.g., restaurant, gym, shopping mall) owners in growing their business. To recommend the best set of customers, PLUTUS takes three main aspects into consideration: (1) Social aspect, (2) Spatial aspect, and (3) Users opinions aspect. To this end, PLUTUS proposes two main algorithms: (1) Profit Calculation: It is responsible of calculating the total profit that a user u may add to a venue v taking into account the social, spatial, and user opinions aspects. (2) Profit Maximization: This algorithm is used to maximize the total profit of a given venue. We evaluated PLUTUS using real data set extracted from an existing Location-based Social Networking website, Foursquare. The results show that Plutus achieves higher estimated profit and more efficient profit calculation than naive marketing algorithms.

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/77QT738U

Prasun Dewan, Vahid Mashaekhi, and Johh Riedl. 2013. Infrastructure and Applications for Collaborative Software Engineering. In Coordination Theory and Collaboration Technology, Gary M. Olson, Thomas W. Malone and John B. Smith (eds.). Psychology Press.

abstract:

The National Science Foundation funded the first Coordination Theory and Collaboration Technology initiative to look at systems that support collaborations in business and elsewhere. This book explores the global revolution in human interconnectedness. It will discuss the various collaborative workgroups and their use in technology. The initiative focuses on processes of coordination and cooperation among autonomous units in human systems, in computer and communication systems, and in hybrid organizations of both systems. This initiative is motivated by three scientific issues which have been the focus of separate research efforts, but which may benefit from collaborative research. The first is the effort to discover the principles underlying how people collaborate and coordinate work efficiently and productively in environments characterized by a high degree of decentralized computation and decision making. The second is to gain a better fundamental understanding of the structure and outputs of organizations, industries, and markets which incorporate sophisticated, decentralized information and communications technology as an important component of their operations. The third is to understand problems of coordination in decentralized or open computer systems.

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/IJI6FSCD

Aaron Halfaker, R. Stuart Geiger, Jonathan T. Morgan, and John Riedl. 2013. The Rise and Decline of an Open Collaboration System How Wikipedia’s Reaction to Popularity Is Causing Its Decline. American Behavioral Scientist 57, 5: 664–688. https://doi.org/10.1177/0002764212469365

abstract:

Open collaboration systems, such as Wikipedia, need to maintain a pool of volunteer contributors to remain relevant. Wikipedia was created through a tremendous number of contributions by millions of contributors. However, recent research has shown that the number of active contributors in Wikipedia has been declining steadily for years and suggests that a sharp decline in the retention of newcomers is the cause. This article presents data that show how several changes the Wikipedia community made to manage quality and consistency in the face of a massive growth in participation have ironically crippled the very growth they were designed to manage. Specifically, the restrictiveness of the encyclopedia’s primary quality control mechanism and the algorithmic tools used to reject contributions are implicated as key causes of decreased newcomer retention. Furthermore, the community’s formal mechanisms for norm articulation are shown to have calcified against changes—especially changes proposed by newer editors.

links and other notes:

• http://abs.sagepub.com/content/57/5/664
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NEFDZPPS

John Riedl. 2013. Crowdfunding Technology Innovation. Computer 46, 3: 100–103. https://doi.org/10.1109/MC.2013.89

abstract:

Sites like Kickstarter fill an important funding role, supporting small, low-risk projects that would otherwise struggle to get the attention of venture capitalists or angel investors.

links and other notes:

• http://doi.ieeecomputersociety.org/10.1109/MC.2013.89
• http://dx.doi.org/10.1109/MC.2013.89
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ZVQ9W9F6

Jonathan Grudin, Gloria Mark, and John Riedl. 2013. Conference-journal Hybrids. Commun. ACM 56, 1: 44–49. https://doi.org/10.1145/2398356.2398371

abstract:

Numerous proposals and experiments have addressed the stresses resulting from computer science’s shift from a journal to a conference publication focus, discussed in over two dozen commentaries in Communications, three panels at CRA Snowbird conferences, the Workshop on Organizing Workshops, Conferences and Symposia for Computer Systems (WOWCS’08), and a recent Dagstuhl workshop. We focus here on recent efforts to blend features of conferences and journals, highlighting a conference that incorporated a revision cycle without increasing the overall reviewer workload. We also survey a range of approaches to improving conference reviewing and management.

links and other notes:

• http://dl.acm.org/authorize?N96556
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/BH9R8KQC

Anthony Jameson and John Riedl. 2013. Introduction to the Special Issue on Highlights of the Decade in Interactive Intelligent Systems. ACM Trans. Interact. Intell. Syst. 2, 4: 19:1-19:3. https://doi.org/10.1145/2395123.2395124

abstract:

This editorial introduction explains the motivation and origin of the TiiS special issue on Highlights of the Decade in Interactive Intelligent Systems and shows how its five articles exemplify the types of research contribution that TiiS aims to encourage and publish.

links and other notes:

• http://dl.acm.org/authorize?N97802
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/W6IERBG5

Kate Grandprey-Shores, Joseph A. Konstan, John T. Riedl, Andrew J. Taylor, Daniel J. Steinberger, and Terrence J. Adam. 2013. Radiology diagnostic exchange agents: clinical term identification and validation. AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science 2013: 244–248. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845772/

abstract:

Diagnostic radiology is one of the key areas of clinical diagnosis on which much of the health care system is built. Along with pathology, radiology has a unique role in providing diagnostic information for prognosis, treatment, and management of clinical conditions. This role is clinically challenging due to the problems of knowledge management associated with the free-text radiology reports which are currently the standard of practice for radiology care. In order to address this critical knowledge management problem, we have proposed a solution using the Radiology Diagnostics Exchange Agent, which is under development and will enhance clinical care management. Using a human computation approach, we have started to identify and validate clinically actionable terms on which an information management infrastructure can be developed with important implications for clinical care and research.

links and other notes:

• http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845772/
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/4JFGA3VA

Morten Warncke-Wang, Dan Cosley, and John Riedl. 2013. Tell Me More: An Actionable Quality Model for Wikipedia. In Proceedings of the 9th International Symposium on Open Collaboration (WikiSym ’13), 8:1-8:10. https://doi.org/10.1145/2491055.2491063

abstract:

In this paper we address the problem of developing actionable quality models for Wikipedia, models whose features directly suggest strategies for improving the quality of a given article. We first survey the literature in order to understand the notion of article quality in the context of Wikipedia and existing approaches to automatically assess article quality. We then develop classification models with varying combinations of more or less actionable features, and find that a model that only contains clearly actionable features delivers solid performance. Lastly we discuss the implications of these results in terms of how they can help improve the quality of articles across Wikipedia.

links and other notes:

• http://dl.acm.org/authorize?N95285
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/H7WMN9PS

Mikhil Masli, Landon Bouma, Andrew Owen, and Loren Terveen. 2013. Geowiki + route analysis = improved transportation planning. In 2013 conference on Computer supported cooperative work companion, 213. https://doi.org/10.1145/2441955.2442008

abstract:

This poster describes the design of a novel route analysis tool based on a community-driven, geographic wiki to assist transportation planners to make better decisions. We highlight the advantages of our tool over other, similar ones---gained due to the use of a wiki-based platform---through a real-life usage scenario.

links and other notes:

• http://dl.acm.org/authorize?N00011
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CU2J2MVM

Tien T. Nguyen and John Riedl. 2013. Predicting Users’ Preference from Tag Relevance. In User Modeling, Adaptation, and Personalization, Sandra Carberry, Stephan Weibelzahl, Alessandro Micarelli and Giovanni Semeraro (eds.). Springer Berlin Heidelberg, 274–280. Retrieved April 9, 2015 from http://link.springer.com/chapter/10.1007/978-3-642-38844-6_23

abstract:

Tagging has become a powerful means for users to find, organize, understand and express their ideas about online entities. However, tags present great challenges when researchers try to incorporate them into the prediction task of recommender systems. In this paper, we propose a novel approach to infer user preference from tag relevance, an indication of how strong each tag applies to each item in recommender systems. We also present a methodology to choose tags that tell most about each user’s preference. Our preliminary results show that at certain levels, some of our algorithms perform better than previous work.

links and other notes:

• http://grouplens.org/site-content/uploads/2013/09/10.1007-978-3-642-38844-6_23.pdf
• http://link.springer.com/chapter/10.1007/978-3-642-38844-6_23
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3SD7687A

Tien T. Nguyen, Daniel Kluver, Ting-Yu Wang, Pik-Mai Hui, Michael D. Ekstrand, Martijn C. Willemsen, and John Riedl. 2013. Rating Support Interfaces to Improve User Experience and Recommender Accuracy. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys ’13), 149–156. https://doi.org/10.1145/2507157.2507188

abstract:

One of the challenges for recommender systems is that users struggle to accurately map their internal preferences to external measures of quality such as ratings. We study two methods for supporting the mapping process: (i) reminding the user of characteristics of items by providing personalized tags and (ii) relating rating decisions to prior rating decisions using exemplars. In our study, we introduce interfaces that provide these methods of support. We also present a set of methodologies to evaluate the efficacy of the new interfaces via a user experiment. Our results suggest that presenting exemplars during the rating process helps users rate more consistently, and increases the quality of the data.

links and other notes:

• http://dl.acm.org/authorize?N95124
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/H3JD6SK8

Brent Hecht, Johannes Schöning, Muki Haklay, Licia Capra, Afra J. Mashhadi, Loren Terveen, and Mei-Po Kwan. 2013. Geographic human-computer interaction. In CHI ’13 Extended Abstracts on Human Factors in Computing Systems, 3163. https://doi.org/10.1145/2468356.2479637

abstract:

Geography is playing an increasingly important role in areas of HCI ranging from social computing to natural user interfaces. At the same time, research in geography has focused more and more on technology-mediated interaction with spatiotemporal phenomena. Despite the growing popularity of this geographic human-computer interaction (GeoHCI) in both fields, there have been few opportunities for GeoHCI knowledge sharing, knowledge creation or community building in either discipline, let alone between them. The goal of this workshop is thus two-fold. First, we will seek to sum up the state of GeoHCI knowledge and address GeoHCI core issues by inviting prominent researchers in the space to share and discuss the most important high-level findings from their work. Second, through our interdisciplinary organizing committee, we will recruit participants from both fields, with the goal of laying the groundwork for a community that works across intra- and interdisciplinary boundaries.

links and other notes:

• http://dl.acm.org/authorize?N00010
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/MD6RP9Z2

Aaron Halfaker, R. Stuart Geiger, Cliff Lampe, Loren Terveen, Amy Bruckman, Brian Keegan, Aniket Kittur, and Geraldine Fitzpatrick. 2013. Community, impact and credit: where should i submit my papers? In 2013 conference on Computer supported cooperative work companion, 89. https://doi.org/10.1145/2441955.2441980

abstract:

We (the authors of CSCWs program) have finite time and energy that can be invested into our publications and the research communities we value. While we want our work to have the most impact possible, we also want to grow and support productive research communities within which to have this impact. This panel discussion explores the costs and benefits of submitting papers to various tiers of conferences and journals surrounding CSCW and reflects on the value of investing hours into building up a research community.

links and other notes:

• http://dl.acm.org/authorize?N00012
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/5VEW3NH5

Colin DeLong, Loren Terveen, and Jaideep Srivastava. 2013. TeamSkill and the NBA: applying lessons from virtual worlds to the real-world. In 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 156–161. https://doi.org/10.1145/2492517.2492628

abstract:

In this paper, we build on our previous work by evaluating several approaches for assessing the skill of players and teams on the basis of both individual performance and group cohesion, or "team chemistry", using game data from the National Basketball Association (NBA). Previously developed for skill assessment in team-based multi-player video games (e.g., Halo 3), we find that group cohesion is a predictive feature in virtual and real-world team-based games, and that methods utilizing such features can often outperform the baseline in both contexts. Additionally, we observe a strong positive correlation between the predictive accuracy of our group cohesion-based approaches and the duration of playing time between a particular configuration of players on a team and their opponents, or "match-up" length.

links and other notes:

• http://dl.acm.org/authorize?N00019
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NPZ463BI

John Riedl and Eric Riedl. 2013. Crowdsourcing Medical Research. Computer 46, 89–92. Retrieved from http://dx.doi.org/10.1109/MC.2013.15

abstract:

Researchers can use data from social websites dedicated to patients to develop a deeper understanding of disease processes and devise more effective treatments.

links and other notes:

• http://dl.acm.org/citation.cfm?id=2478957
• http://www.computer.org/csdl/mags/co/2013/01/mco2013010089-abs.html
• http://dx.doi.org/10.1109/MC.2013.15
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/765C3965

R. Stuart Geiger and Aaron Halfaker. 2013. Using Edit Sessions to Measure Participation in Wikipedia. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW ’13), 861–870. https://doi.org/10.1145/2441776.2441873

abstract:

Many quantitative, log-based studies of participation and contribution in CSCW and CMC systems measure the activity of users in terms of output, based on metrics like posts to forums, edits to Wikipedia articles, or commits to code repositories. In this paper, we instead seek to estimate the amount of time users have spent contributing. Through an analysis of Wikipedia log data, we identify a pattern of punctuated bursts in editors' activity that we refer to as edit sessions. Based on these edit sessions, we build a metric that approximates the labor hours of editors in the encyclopedia. Using this metric, we first compare labor-based analyses with output-based analyses, finding that the activity of many editors can appear quite differently based on the kind of metric used. Second, we use edit session data to examine phenomena that cannot be adequately studied with purely output-based metrics, such as the total number of labor hours for the entire project.

links and other notes:

• http://doi.acm.org/10.1145/2441776.2441873
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/WHIKNHVN

Aaron Halfaker, Oliver Keyes, and Dario Taraborelli. 2013. Making Peripheral Participation Legitimate: Reader Engagement Experiments in Wikipedia. In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (CSCW ’13), 849–860. https://doi.org/10.1145/2441776.2441872

abstract:

Open collaboration communities thrive when participation is plentiful. Recent research has shown that the English Wikipedia community has constructed a vast and accurate information resource primarily through the monumental effort of a relatively small number of active, volunteer editors. Beyond Wikipedia's active editor community is a substantially larger pool of potential participants: readers. In this paper we describe a set of field experiments using the Article Feedback Tool, a system designed to elicit lightweight contributions from Wikipedia's readers. Through the lens of social learning theory and comparisons to related work in open bug tracking software, we evaluate the costs and benefits of the expanded participation model and show both qualitatively and quantitatively that peripheral contributors add value to an open collaboration community as long as the cost of identifying low quality contributions remains low.

links and other notes:

• http://doi.acm.org/10.1145/2441776.2441872
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/7VXP5S6G

Eric Gilbert, Saeideh Bakhshi, Shuo Chang, and Loren Terveen. 2013. “I Need to Try This”?: A Statistical Overview of Pinterest. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’13), 2427–2436. https://doi.org/10.1145/2470654.2481336

abstract:

Over the past decade, social network sites have become ubiquitous places for people to maintain relationships, as well as loci of intense research interest. Recently, a new site has exploded into prominence: Pinterest became the fastest social network to reach 10M users, growing 4000% in 2011 alone. While many Pinterest articles have appeared in the popular press, there has been little scholarly work so far. In this paper, we use a quantitative approach to study three research questions about the site. What drives activity on Pinterest? What role does gender play in the site's social connections? And finally, what distinguishes Pinterest from existing networks, in particular Twitter? In short, we find that being female means more repins, but fewer followers, and that four verbs set Pinterest apart from Twitter: use, look, want and need. This work serves as an early snapshot of Pinterest that later work can leverage.

links and other notes:

• http://dl.acm.org/authorize?N95274
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UEAGUSM2

Shuo Chang and Aditya Pal. 2013. Routing Questions for Collaborative Answering in Community Question Answering. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM ’13), 494–501. https://doi.org/10.1145/2492517.2492559

abstract:

Community Question Answering (CQA) service enables its users to exchange knowledge in the form of questions and answers. By allowing the users to contribute knowledge, CQA not only satisfies the question askers but also provides valuable references to other users with similar queries. Due to a large volume of questions, not all questions get fully answered. As a result, it can be useful to route a question to a potential answerer. In this paper, we present a question routing scheme which takes into account the answering, commenting and voting propensities of the users. Unlike prior work which focuses on routing a question to the most desirable expert, we focus on routing it to a group of users - who would be willing to collaborate and provide useful answers to that question. Through empirical evidence, we show that more answers and comments are desirable for improving the lasting value of a question-answer thread. As a result, our focus is on routing a question to a team of compatible users. We propose a recommendation model that takes into account the compatibility, topical expertise and availability of the users. Our experiments over a large real-world dataset shows the effectiveness of our approach over several baseline models.

links and other notes:

• http://www-users.cs.umn.edu/~schang/papers/13ASONAM.pdf
• http://doi.acm.org/10.1145/2492517.2492559
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/GZTNNMJ6

Joseph A. Konstan and Gediminas Adomavicius. 2013. Toward Identification and Adoption of Best Practices in Algorithmic Recommender Systems Research. In Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation (RepSys ’13), 23–28. https://doi.org/10.1145/2532508.2532513

abstract:

One of the goals of data-intensive research, in any field of study, is to grow knowledge over time as additional studies contribute to collective knowledge and understanding. Two steps are critical to making such research cumulative -- the individual research results need to be documented thoroughly and conducted on data made available to others (to allow replication and meta-analysis), and the individual research needs to be carried out correctly, following standards and best practices for coding, missing data, algorithm choices, algorithm implementations, metrics, and statistics. This work aims to address a growing concern that the Recommender Systems research community (which is uniquely equipped to address many important challenges in electronic commerce, social networks, social media, and big data settings) is facing a crisis where a significant number of research papers lack the rigor and evaluation to be properly judged and, therefore, have little to contribute to collective knowledge. We advocate that this issue can be addressed through development and dissemination (to authors, reviewers, and editors) of best-practice research methodologies, resulting in specific guidelines and checklists, as well as through tool development to support effective research. We also plan to assess the impact on the field with an eye toward supporting such efforts in other data-intensive specialties.

links and other notes:

• http://dl.acm.org/authorize?N95273
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/DB65EU8J

Fernando Mourão, Leonardo Rocha, Joseph A. Konstan, and Wagner Meira Jr. 2013. Exploiting Non-content Preference Attributes Through Hybrid Recommendation Method. In Proceedings of the 7th ACM Conference on Recommender Systems (RecSys ’13), 177–184. https://doi.org/10.1145/2507157.2507179

abstract:

This paper explores a method for incorporating into a recommender system explicit representations of user's preferences over non-content attributes such as popularity, recency, and similarity of recommended items. We show how such attributes can be modeled as a preference vector that can be used in a vector-space content-based recommender, and how that content-based recommender can be integrated with various collaborative filtering techniques through re-weighting of Top-M recommendations. We evaluate this approach on several recommender systems datasets and collaborative filtering methods, and find that incorporating the three preference attributes can lead to a substantial increase in Top-50 precision while also enhancing diversity and novelty.

links and other notes:

• http://dl.acm.org/authorize?N95135
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/E7C66DN6

F. Bi and J.A. Konstan. 2012. Customer Service 2.0: Where Social Computing Meets Customer Relations. Computer 45, 11: 93–95. https://doi.org/10.1109/MC.2012.370

abstract:

Social computing is moving customer relations from private spaces into the public space.

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/9X979B2Q

S. Andrew Sheppard. 2012. wq: A modular framework for collecting, storing, and utilizing experiential VGI. https://doi.org/10.1145/2442952.2442964

abstract:

links and other notes:

• https://wq.io/research/framework
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ET9DHB6H

Yuqing Ren, F. Maxwell Harper, Sara Drenner, Loren Terveen, Sara Kiesler, John Riedl, and Robert E. Kraut. 2012. Building Member Attachment in Online Communities: Applying Theories of Group Identity and Interpersonal Bonds. MIS Q. 36, 3: 841–864. Retrieved April 30, 2015 from http://dl.acm.org/citation.cfm?id=2481655.2481665

abstract:

Online communities are increasingly important to organizations and the general public, but there is little theoretically based research on what makes some online communities more successful than others. In this article, we apply theory from the field of social psychology to understand how online communities develop member attachment, an important dimension of community success. We implemented and empirically tested two sets of community features for building member attachment by strengthening either group identity or interpersonal bonds. To increase identity-based attachment, we gave members information about group activities and intergroup competition, and tools for group-level communication. To increase bond-based attachment, we gave members information about the activities of individual members and interpersonal similarity, and tools for interpersonal communication. Results from a six-month field experiment show that participants' visit frequency and self-reported attachment increased in both conditions. Community features intended to foster identity-based attachment had stronger effects than features intended to foster bond-based attachment. Participants in the identity condition with access to group profiles and repeated exposure to their group's activities visited their community twice as frequently as participants in other conditions. The new features also had stronger effects on newcomers than on old-timers. This research illustrates how theory from the social science literature can be applied to gain a more systematic understanding of online communities and how theory-inspired features can improve their success.

links and other notes:

• http://dl.acm.org/citation.cfm?id=2481655.2481665
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/CBEG6SNA

Jesse Vig, Shilad Sen, and John Riedl. 2012. The Tag Genome: Encoding Community Knowledge to Support Novel Interaction. ACM Trans. Interact. Intell. Syst. 2, 3: 13:1-13:44. https://doi.org/10.1145/2362394.2362395

abstract:

This article introduces the tag genome, a data structure that extends the traditional tagging model to provide enhanced forms of user interaction. Just as a biological genome encodes an organism based on a sequence of genes, the tag genome encodes an item in an information space based on its relationship to a common set of tags. We present a machine learning approach for computing the tag genome, and we evaluate several learning models on a ground truth dataset provided by users. We describe an application of the tag genome called Movie Tuner which enables users to navigate from one item to nearby items along dimensions represented by tags. We present the results of a 7-week field trial of 2,531 users of Movie Tuner and a survey evaluating users’ subjective experience. Finally, we outline the broader space of applications of the tag genome.

links and other notes:

• http://www.grouplens.org/system/files/tag_genome.pdf
• http://dl.acm.org/authorize?N97561
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/WTSMDMI2

Anuradha Uduwage and Joseph Konstan. 2012. The Answer Is Out There: Online Q&A Sites. Computer 45, 7: 90–92. https://doi.org/10.1109/MC.2012.244

abstract:

A diverse set of online question and answer sites has emerged for users seeking information beyond simple search queries.

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/IQ8WU28C

Zhenhua Dong, Chuan Shi, Shilad Sen, Loren Terveen, and John Riedl. 2012. War Versus Inspirational in Forrest Gump: Cultural Effects in Tagging Communities. In Sixth International AAAI Conference on Weblogs and Social Media. Retrieved April 30, 2015 from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4624

abstract:

People from different cultures vary in cognition, emotion, and behavior. We explore cultural differences in a tagging system. We developed a model of cultural differences and performed a controlled empirical study with American and Chinese subjects to investigate questions that arise from the model. American and Chinese subjects differed in many ways: the number and types of tags they applied; the extent to which they applied suggested tags or entered new tags of their own; and how often they applied tags that originated from a different culture. Our results are consistent with theories of cultural differences between Asian and Western cultures. Our findings suggest new opportunities and mechanisms for shaping user behavior to produce useful tag repositories.

links and other notes:

• http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4624
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/VVVC5FAT

R. Stuart Geiger, Aaron Halfaker, Maryana Pinchuk, and Steven Walling. 2012. Defense Mechanism or Socialization Tactic? Improving Wikipedia’s Notifications to Rejected Contributors. In Sixth International AAAI Conference on Weblogs and Social Media. Retrieved April 30, 2015 from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4657

abstract:

Unlike traditional firms, open collaborative systems rely on volunteers to operate, and many communities struggle to maintain enough contributors to ensure the quality and quantity of content. However, Wikipedia has historically faced the exact opposite problem: too much participation, particularly from users who, knowingly or not, do not share the same norms as veteran Wikipedians. During its period of exponential growth, the Wikipedian community developed specialized socio-technical defense mechanisms to protect itself from the negatives of massive participation: spam, vandalism, falsehoods, and other damage. Yet recently, Wikipedia has faced a number of high-profile issues with recruiting and retaining new contributors. In this paper, we first illustrate and describe the various defense mechanisms at work in Wikipedia, which we hypothesize are inhibiting newcomer retention. Next, we present results from an experiment aimed at increasing both the quantity and quality of editors by altering various elements of these defense mechanisms, specifically pre-scripted warnings and notifications that are sent to new editors upon reverting or rejecting contributions. Using logistic regressions to model new user activity, we show which tactics work best for different populations of users based on their motivations when joining Wikipedia. In particular, we found that personalized messages in which Wikipedians identified themselves in active voice and took direct responsibility for rejecting an editor’s contributions were much more successful across a variety of outcome metrics than the current messages, which typically use an institutional and passive voice.

links and other notes:

• http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4657
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2HGW33SG

Aditya Pal, Shuo Chang, and Joseph A. Konstan. 2012. Evolution of Experts in Question Answering Communities. In Sixth International AAAI Conference on Weblogs and Social Media. Retrieved April 10, 2015 from http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4653

abstract:

Community Question Answering (CQA) services thrive as a result of a small number of highly active users, typically called experts, who provide a large number of high quality useful answers. Understanding the temporal dynamics and interactions between experts can present key insights into how community members evolve over time. In this paper, we present a temporal study of experts in CQA and analyze the changes in their behavioral patterns over time. Further, using unsupervised machine learning methods, we show the interesting evolution patterns that can help us distinguish experts from one another. Using supervised classification methods, we show that the models based on evolutionary data of users can be more effective at expert identification than the models that ignore evolution. We run our experiments on two large online CQA to show the generality of our proposed approach.

links and other notes:

• http://www-users.cs.umn.edu/~apal/res/icwsm12_expertevolution.pdf
• http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4653
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/USTFSR25

Aditya Pal, F. Maxwell Harper, and Joseph A. Konstan. 2012. Exploring Question Selection Bias to Identify Experts and Potential Experts in Community Question Answering. ACM Trans. Inf. Syst. 30, 2: 10:1-10:28. https://doi.org/10.1145/2180868.2180872

abstract:

Community Question Answering (CQA) services enable their users to exchange knowledge in the form of questions and answers. These communities thrive as a result of a small number of highly active users, typically called experts, who provide a large number of high-quality useful answers. Expert identification techniques enable community managers to take measures to retain the experts in the community. There is further value in identifying the experts during the first few weeks of their participation as it would allow measures to nurture and retain them. In this article we address two problems: (a) How to identify current experts in CQA? and (b) How to identify users who have potential of becoming experts in future (potential experts)? In particular, we propose a probabilistic model that captures the selection preferences of users based on the questions they choose for answering. The probabilistic model allows us to run machine learning methods for identifying experts and potential experts. Our results over several popular CQA datasets indicate that experts differ considerably from ordinary users in their selection preferences; enabling us to predict experts with higher accuracy over several baseline models. We show that selection preferences can be combined with baseline measures to improve the predictive performance even further.

links and other notes:

• http://dl.acm.org/authorize?N97567
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/JD5I8XJ7

Joseph A. Konstan and John Riedl. 2012. Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction 22, 1–2: 101–123. https://doi.org/10.1007/s11257-011-9112-x

abstract:

Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user experience with the recommender. We show through examples that the embedding of the algorithm in the user experience dramatically affects the value to the user of the recommender. We argue that evaluating the user experience of a recommender requires a broader set of measures than have been commonly used, and suggest additional measures that have proven effective. Based on our analysis of the state of the field, we identify the most important open research problems, and outline key challenges slowing the advance of the state of the art, and in some cases limiting the relevance of research to real-world applications.

links and other notes:

• http://link.springer.com/article/10.1007/s11257-011-9112-x
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/8SEV8WMP

Fernando Torre, David Pitchford, Phil Brown, and Loren Terveen. 2012. Matching GPS Traces to (Possibly) Incomplete Map Data: Bridging Map Building and Map Matching. In Proceedings of the 20th International Conference on Advances in Geographic Information Systems (SIGSPATIAL ’12), 546–549. https://doi.org/10.1145/2424321.2424411

abstract:

Analysis of geographic data often requires matching GPS traces to road segments. Unfortunately, map data is often incomplete, resulting in failed or incorrect matches. In this paper, we extend an HMM map-matching algorithm to handle missing blocks. We test our algorithm using map data from the Cyclopath geowiki and GPS traces from Cyclopath's mobile app. Even for conservative cutoff distances, our algorithm found a significant amount of missing data per set of GPS traces. We tested the algorithm for accuracy by removing existing blocks from our map dataset. As the cutoff distance was lowered, false negatives were decreased from 34% to 16% as false positives increased from 5% to 10%. Although the algorithm degrades with increasing amounts of missing data, our results show that our extensions have the potential to improve both map matches and map data.

links and other notes:

• http://dl.acm.org/authorize?N97569
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FIPGEXGC

Mikhil Masli and Loren Terveen. 2012. Evaluating Compliance-without-pressure Techniques for Increasing Participation in Online Communities. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’12), 2915–2924. https://doi.org/10.1145/2207676.2208698

abstract:

Social psychology offers several theories of potential use for designing techniques to increase user contributions to online communities. Some of these techniques follow the "compliance without pressure" approach, where users are led to comply with a request without being subjected to any obvious external pressure. We evaluated two such techniques -- foot-in-the-door and low-ball -- in the context of Cyclopath, a geographic wiki. We found that while both techniques succeeded, low-ball elicited more work than foot-in-the-door. We discuss design and research implications of applying these (and other such techniques) in online communities.

links and other notes:

• http://grouplens.org/site-content/uploads/2013/09/evaluatingcompliance.pdf
• http://doi.acm.org/10.1145/2207676.2208698
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NQNET6QG

Daniel Kluver, Tien T. Nguyen, Michael Ekstrand, Shilad Sen, and John Riedl. 2012. How Many Bits Per Rating? In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12), 99–106. https://doi.org/10.1145/2365952.2365974

abstract:

Most recommender systems assume user ratings accurately represent user preferences. However, prior research shows that user ratings are imperfect and noisy. Moreover, this noise limits the measurable predictive power of any recommender system. We propose an information theoretic framework for quantifying the preference information contained in ratings and predictions. We computationally explore the properties of our model and apply our framework to estimate the efficiency of different rating scales for real world datasets. We then estimate how the amount of information predictions give to users is related to the scale ratings are collected on. Our findings suggest a tradeoff in rating scale granularity: while previous research indicates that coarse scales (such as thumbs up / thumbs down) take less time, we find that ratings with these scales provide less predictive value to users. We introduce a new measure, preference bits per second, to quantitatively reconcile this tradeoff.

links and other notes:

• http://dl.acm.org/authorize?N97560
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/EKA4NAAQ

Michael Ekstrand and John Riedl. 2012. When Recommenders Fail: Predicting Recommender Failure for Algorithm Selection and Combination. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys ’12), 233–236. https://doi.org/10.1145/2365952.2366002

abstract:

Hybrid recommender systems --- systems using multiple algorithms together to improve recommendation quality --- have been well-known for many years and have shown good performance in recent demonstrations such as the NetFlix Prize. Modern hybridization techniques, such as feature-weighted linear stacking, take advantage of the hypothesis that the relative performance of recommenders varies by circumstance and attempt to optimize each item score to maximize the strengths of the component recommenders. Less attention, however, has been paid to understanding what these strengths and failure modes are. Understanding what causes particular recommenders to fail will facilitate better selection of the component recommenders for future hybrid systems and a better understanding of how individual recommender personalities can be harnessed to improve the recommender user experience. We present an analysis of the predictions made by several well-known recommender algorithms on the MovieLens 10M data set, showing that for many cases in which one algorithm fails, there is another that will correctly predict the rating.

links and other notes:

• http://dl.acm.org/authorize?N97562
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/S75FI2SV

Michael Bernstein, Jacob Wobbrock, Sarita Yardi, Dan Cosley, Carl DiSalvo, Sanjay Kairam, David Karger, Travis Kriplean, Cliff Lampe, Wendy Mackay, and Loren Terveen. 2012. Reject me: peer review and SIGCHI. In CHI ’12 Extended Abstracts on Human Factors in Computing Systems, 1197. https://doi.org/10.1145/2212776.2212422

abstract:

The HCI research community grows bigger each year, refining and expanding its boundaries in new ways. The ability to effectively review submissions is critical to the growth of CHI and related conferences. The review process is designed to produce a consistent supply of fair, high-quality reviews without overloading individual reviewers; yet, after each cycle, concerns are raised about limitations of the process. Every year, participants are left wondering why their papers were not accepted (or why they were). This SIG will explore reviewing through a critical and constructive lens, discussing current successes and future opportunities in the CHI review process. Goals will include actionable conclusions about ways to improve the system, potential alternative peer models, and the creation of materials to educate newcomer reviewers.

links and other notes:

• http://dl.acm.org/authorize?N00014
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TFH3GMRZ

Lucy E. Dunne, Jingwen Zhang, and Loren Terveen. 2012. An investigation of contents and use of the home wardrobe. In 2012 ACM Conference on Ubiquitous Computing, 203. https://doi.org/10.1145/2370216.2370247

abstract:

The home wardrobe is a complex and variable system, interacted with daily by its user/manager in a time- and resource-constrained decision-making process. Ubiquitous computing technology offers advantages in augmenting the decision-making process, and the potential to simultaneously encourage sustainable behaviors. In this study we present an empirical analysis of the contents of 11 home wardrobes and 3-6 months of daily dressing decisions for 5 users. We find that an average of only 7% of our female participants' wardrobes and 47% of our male participants' wardrobes are in regular use. In addition, we present an analysis of wardrobe contents, outfit composition, and garment utility in the wardrobe.

links and other notes:

• http://dl.acm.org/citation.cfm?doid=2370216.2370247
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/VRBG2RT2

Aaron Halfaker and John Riedl. 2012. Bots and Cyborgs: Wikipedia’s Immune System. Computer 45, 79–82.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/HM556Q7X

Rosta Farzan, Robert Kraut, Aditya Pal, and Joseph Konstan. 2012. Socializing Volunteers in an Online Community: A Field Experiment. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (CSCW ’12), 325–334. https://doi.org/10.1145/2145204.2145256

abstract:

Although many off-line organizations give their employees training, mentorship, a cohort and other socialization experiences that improve their retention and productivity, online production communities rarely do this. This paper describes the planning, execution and evaluation of a socialization regime for an online technical support community. In a two-phase project, we first automatically identified from participants' early behavior, those with high potential to become core members. We then designed, delivered and experimentally evaluated socialization experiences intended to build commitment and competence among these potential core members. We were able to identify potential core members with high accuracy from only two weeks of behavior. A year later, those classified as potential core members participated in the community ten times more actively than those not identified. In an evaluation experiment, some potential core members were randomly assigned to receive socialization experiences, while others were not. A year later, those who had participated in the socialization regime contributed more answers in the community compared to those in the control condition. The socialization experiences, however, undercut their sense of connection to the community and the quality of their contributions. We discuss what was effective and what could be improved in designing socialization experiences for online groups.

links and other notes:

• http://dl.acm.org/authorize?62233
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/F4QWEZVT

Morten Warncke-Wang, Anuradha Uduwage, Zhenhua Dong, and John Riedl. 2012. In Search of the ur-Wikipedia: Universality, Similarity, and Translation in the Wikipedia Inter-language Link Network. In Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration (WikiSym ’12), 20:1-20:10. https://doi.org/10.1145/2462932.2462959

abstract:

Wikipedia has become one of the primary encyclopaedic information repositories on the World Wide Web. It started in 2001 with a single edition in the English language and has since expanded to more than 20 million articles in 283 languages. Criss-crossing between the Wikipedias is an inter-language link network, connecting the articles of one edition of Wikipedia to another. We describe characteristics of articles covered by nearly all Wikipedias and those covered by only a single language edition, we use the network to understand how we can judge the similarity between Wikipedias based on concept coverage, and we investigate the flow of translation between a selection of the larger Wikipedias. Our findings indicate that the relationships between Wikipedia editions follow Tobler's first law of geography: similarity decreases with increasing distance. The number of articles in a Wikipedia edition is found to be the strongest predictor of similarity, while language similarity also appears to have an influence. The English Wikipedia edition is by far the primary source of translations. We discuss the impact of these results for Wikipedia as well as user-generated content communities in general.

links and other notes:

• http://dl.acm.org/authorize?N97563
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/GATJJCPU

Aditya Pal, James Margatan, and Joseph Konstan. 2012. Question Temporality: Identification and Uses. In Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (CSCW ’12), 257–260. https://doi.org/10.1145/2145204.2145246

abstract:

links and other notes:

• http://dl.acm.org/authorize?N97568
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/4QM69BD2

Wei Li, Justin Matejka, Tovi Grossman, Joseph A. Konstan, and George Fitzmaurice. 2011. Design and Evaluation of a Command Recommendation System for Software Applications. ACM Trans. Comput.-Hum. Interact. 18, 2: 6:1-6:35. https://doi.org/10.1145/1970378.1970380

abstract:

We examine the use of modern recommender system technology to aid command awareness in complex software applications. We first describe our adaptation of traditional recommender system algorithms to meet the unique requirements presented by the domain of software commands. A user study showed that our item-based collaborative filtering algorithm generates 2.1 times as many good suggestions as existing techniques. Motivated by these positive results, we propose a design space framework and its associated algorithms to support both global and contextual recommendations. To evaluate the algorithms, we developed the CommunityCommands plug-in for AutoCAD. This plug-in enabled us to perform a 6-week user study of real-time, within-application command recommendations in actual working environments. We report and visualize command usage behaviors during the study, and discuss how the recommendations affected users behaviors. In particular, we found that the plug-in successfully exposed users to new commands, as unique commands issued significantly increased.

links and other notes:

• http://dl.acm.org/authorize?N97574
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/DWAV4DXS

Mikhil N. Masli, Reid Priedhorsky, and Loren Terveen. 2011. Task Specialization in Social Production Communities: The Case of Geographic Volunteer Work. In Fifth International AAAI Conference on Weblogs and Social Media. Retrieved April 30, 2015 from http://webcache.googleusercontent.com/search?q=cache:axkUNnhhLDwJ:www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/download/2747/3308+&cd=1&hl=en&ct=clnk&gl=us

abstract:

In social production communities, users' individual and collective efforts lead to the creation of valuable resources — cf. Wikipedia, Open Street Map, and Reddit. Contributors to such communities often specialize in the tasks they choose to do. We found evidence for specialization by work type in Cyclopath, a geographic wiki for bicyclists -- most users edit a single type of map feature, such as points of interest or roads and trails. We also saw a user lifecycle effect: as users gain experience, they specialize in editing roads and trails. Our findings suggest more effective ways to organize social production interfaces, compose units of work, and match them to users who want to help.

links and other notes:

• http://webcache.googleusercontent.com/search?q=cache:axkUNnhhLDwJ:www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/download/2747/3308+&cd=1&hl=en&ct=clnk&gl=us
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/8XUSV8JQ

Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan. 2011. Collaborative Filtering Recommender Systems. Found. Trends Hum.-Comput. Interact. 4, 2: 81–173. https://doi.org/10.1561/1100000009

abstract:

Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users to filter through large information and product spaces. Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance. Research in the field is moving in the direction of a richer understanding of how recommender technology may be embedded in specific domains. The differing personalities exhibited by different recommender algorithms show that recommendation is not a one-size-fits-all problem. Specific tasks, information needs, and item domains represent unique problems for recommenders, and design and evaluation of recommenders needs to be done based on the user tasks to be supported. Effective deployments must begin with careful analysis of prospective users and their goals. Based on this analysis, system designers have a host of options for the choice of algorithm and for its embedding in the surrounding user experience. This paper discusses a~wide variety of the choices available and their implications, aiming to provide both practicioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.

links and other notes:

• http://files.grouplens.org/papers/FnT%20CF%20Recsys%20Survey.pdf
• http://dx.doi.org/10.1561/1100000009
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/P8ASCNAP

Aditya Pal, Rosta Farzan, Joseph A. Konstan, and Robert E. Kraut. 2011. Early Detection of Potential Experts in Question Answering Communities. In User Modeling, Adaption and Personalization, Joseph A. Konstan, Ricardo Conejo, José L. Marzo and Nuria Oliver (eds.). Springer Berlin Heidelberg, 231–242. Retrieved April 30, 2015 from http://link.springer.com/chapter/10.1007/978-3-642-22362-4_20

abstract:

Question answering communities (QA) are sustained by a handful of experts who provide a large number of high quality answers. Identifying these experts during the first few weeks of their joining the community can be beneficial as it would allow community managers to take steps to develop and retain these potential experts. In this paper, we explore approaches to identify potential experts as early as within the first two weeks of their association with the QA. We look at users’ behavior and estimate their motivation and ability to help others. These qualities enable us to build classification and ranking models to identify users who are likely to become experts in the future. Our results indicate that the current experts can be effectively identified from their early behavior. We asked community managers to evaluate the potential experts identified by our algorithm and their analysis revealed that quite a few of these users were already experts or on the path of becoming experts. Our retrospective analysis shows that some of these potential experts had already left the community, highlighting the value of early identification and engagement.

links and other notes:

• http://dl.acm.org/citation.cfm?id=2021855.2021876
• http://www-users.cs.umn.edu/~apal/res/umap11_earlyexperts.pdf
• http://link.springer.com/chapter/10.1007/978-3-642-22362-4_20
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/JWPXFQD3

Katherine Panciera, Mikhil Masli, and Loren Terveen. 2011. “How Should I Go from ___ to ___ Without Getting Killed?”: Motivation and Benefits in Open Collaboration. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym ’11), 183–192. https://doi.org/10.1145/2038558.2038587

abstract:

Many people rely on open collaboration projects to run their computer (Linux), browse the web (Mozilla Firefox), and get information (Wikipedia). While these projects are successful, many such efforts suffer from lack of participation. Understanding what motivates users to participate and the benefits they perceive from their participation can help address this problem. We examined these issues through a survey of contributors and information consumers in the Cyclopath geographic wiki. We analyzed subject responses to identify a number of key motives and perceived benefits. Based on these results, we articulate several general techniques to encourage more and new forms of participation in open collaboration communities. Some of these techniques have the potential to engage information consumers more deeply and productively in the life of open collaboration communities.

links and other notes:

• http://dl.acm.org/authorize?N97578
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UDIGFI7N

Werner Geyer, Casey Dugan, Beth Brownholtz, Mikhil Masli, Elizabeth Daly, and David R. Millen. 2011. An Open, Social Microcalender for the Enterprise: Timely? In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’11), 247–256. https://doi.org/10.1145/1978942.1978977

abstract:

We present the system design and rational for a novel social microcalendar called Timely. Our system has been inspired by previous research on calendaring and popular social network applications, in particular microblogging. Timely provides an open, social space for enterprise users to share their events, socialize, and discover what else is going on in their network and beyond. A detailed analysis of the events shared by users during the site's first 47 days reveals that users willingly share their time commitments despite an existing culture of restricted calendars.

links and other notes:

• http://doi.acm.org/10.1145/1978942.1978977
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ECZ74SCW

Jesse Vig, Shilad Sen, and John Riedl. 2011. Navigating the Tag Genome. In Proceedings of the 16th International Conference on Intelligent User Interfaces (IUI ’11), 93–102. https://doi.org/10.1145/1943403.1943418

abstract:

Tags help users understand a rich information space, by showing them specific text annotations for each item in the space and enabling them to search by these annotations. Often, however, users may wish to move from one item to other items that are similar overall, but that differ in key characteristics. For example, a user who loves Pulp Fiction might want to see a similar movie, but might be in a mood for a less "dark" movie. This paper introduces Movie Tuner, a novel interface that supports navigation from one item to nearby items along dimensions represented by tags. Movie Tuner is based on a data structure called the tag genome, which is described in separate work. The tag genome encodes each item's relationship to a common set of tags by applying machine learning algorithms to user-contributed content. The present paper discusses our design of Movie Tuner, including algorithms for navigating to new items and for suggesting tags for navigation. We present the results of a 7-week field trial of 2,531 users of Movie Tuner, and of a survey evaluating users' subjective experience.

links and other notes:

• http://dl.acm.org/authorize?N97585
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/486NWK6J

Reid Priedhorsky and Loren Terveen. 2011. Wiki Grows Up: Arbitrary Data Models, Access Control, and Beyond. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym ’11), 63–71. https://doi.org/10.1145/2038558.2038570

abstract:

Ward Cunningham's vision for the wiki was that it would be "the simplest online database that could possibly work". We consider here a common manifestation of simplicity: the assumption that the objects in a wiki that can be edited (e.g., Wikipedia articles) are relatively independent. As wiki applications in new domains emerge, however, this assumption is no longer tenable. In wikis where the objects of interest are highly interdependent (e.g., geographic wikis), fundamental concepts like the revision and undoing must be refined. This is particularly so when fine-grained access control is required (as in enterprise wikis or wikis to support collaboration between citizens and government officials). We explore these issues in the context of the Cyclopath geowiki and present solutions that we have designed and have implemented or are implementing.

links and other notes:

• http://dl.acm.org/authorize?N97572
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/A89C9SMF

David R. Musicant, Yuqing Ren, James A. Johnson, and John Riedl. 2011. Mentoring in Wikipedia: A Clash of Cultures. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym ’11), 173–182. https://doi.org/10.1145/2038558.2038586

abstract:

The continuous success of Wikipedia depends upon its capability to recruit and engage new editors, especially those with new knowledge and perspectives. Yet Wikipedia over the years has become a complicated bureaucracy that may be difficult for newcomers to navigate. Mentoring is a practice that has been widely used in offline organizations to help new members adjust to their roles. In this paper, we draw insights from the offline mentoring literature to analyze mentoring practices in Wikipedia and how they influence editor behaviors. Our quantitative analysis of the Adopt-a-user program shows mixed success of the program. Communication between adopters and adoptees is correlated with the amount of article editing done by adoptees shortly after adoption. Our qualitative analysis of the communication between adopters and adoptees suggests that several key functions of mentoring are missing or not fulfilled consistently. Most adopters focus on establishing their legitimacy rather than acting proactively to guide, protect, and support the long-term growth of adoptees. We conclude with recommendations of how Wikipedia mentoring programs can evolve to take advantage of offline best practices.

links and other notes:

• http://dl.acm.org/authorize?N97579
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2A957JN4

Aaron Halfaker, Aniket Kittur, and John Riedl. 2011. Don’t Bite the Newbies: How Reverts Affect the Quantity and Quality of Wikipedia Work. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym ’11), 163–172. https://doi.org/10.1145/2038558.2038585

abstract:

Reverts are important to maintaining the quality of Wikipedia. They fix mistakes, repair vandalism, and help enforce policy. However, reverts can also be damaging, especially to the aspiring editor whose work they destroy. In this research we analyze 400,000 Wikipedia revisions to understand the effect that reverts had on editors. We seek to understand the extent to which they demotivate users, reducing the workforce of contributors, versus the extent to which they help users improve as encyclopedia editors. Overall we find that reverts are powerfully demotivating, but that their net influence is that more quality work is done in Wikipedia as a result of reverts than is lost by chasing editors away. However, we identify key conditions -- most specifically new editors being reverted by much more experienced editors - under which reverts are particularly damaging. We propose that reducing the damage from reverts might be one effective path for Wikipedia to solve the newcomer retention problem.

links and other notes:

• http://dl.acm.org/authorize?N97577
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/C5DWJANA

Aditya Pal and Scott Counts. 2011. Identifying Topical Authorities in Microblogs. In Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (WSDM ’11), 45–54. https://doi.org/10.1145/1935826.1935843

abstract:

Content in microblogging systems such as Twitter is produced by tens to hundreds of millions of users. This diversity is a notable strength, but also presents the challenge of finding the most interesting and authoritative authors for any given topic. To address this, we first propose a set of features for characterizing social media authors, including both nodal and topical metrics. We then show how probabilistic clustering over this feature space, followed by a within-cluster ranking procedure, can yield a final list of top authors for a given topic. We present results across several topics, along with results from a user study confirming that our method finds authors who are significantly more interesting and authoritative than those resulting from several baseline conditions. Additionally our algorithm is computationally feasible in near real-time scenarios making it an attractive alternative for capturing the rapidly changing dynamics of microblogs.

links and other notes:

• http://doi.acm.org/10.1145/1935826.1935843
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/2SZ84TZJ

Mikhil Masli, Werner Geyer, Casey Dugan, and Beth Brownholtz. 2011. The Design and Usage of Tentative Events for Time-based Social Coordination in the Enterprise. In Proceedings of the 20th International Conference on World Wide Web (WWW ’11), 765–774. https://doi.org/10.1145/1963405.1963512

abstract:

Existing enterprise calendaring systems have suffered from problems like rigidity, lack of transparency, and poor integration with social networks. We present the system design and rationale for a novel social coordination mechanism, called "Suggestions," that addresses these issues. Our system integrates ideas drawn from designs of lightweight polling systems and one's social network into an open calendar tool, providing a space for users to coordinate, socialize around, or negotiate the "what" and the "when" of their events. Suggestions was released inside a large enterprise setting, where initial interviews revealed users' thoughts on transparent scheduling, reaching wider audiences and task appropriateness, and suggested ways to improve our design.

links and other notes:

• http://doi.acm.org/10.1145/1963405.1963512
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RM29KPI6

Jilin Chen, Rowan Nairn, and Ed Chi. 2011. Speak Little and Well: Recommending Conversations in Online Social Streams. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’11), 217–226. https://doi.org/10.1145/1978942.1978974

abstract:

Conversation is a key element in online social streams such as Twitter and Facebook. However, finding interesting conversations to read is often a challenge, due to information overload and differing user preferences. In this work we explored five algorithms that recommend conversations to Twitter users, utilizing thread length, topic and tie-strength as factors. We compared the algorithms through an online user study and gathered feedback from real Twitter users. In particular, we investigated how users' purposes of using Twitter affect user preferences for different types of conversations and the performance of different algorithms. Compared to a random baseline, all algorithms recommended more interesting conversations. Further, tie-strength based algorithms performed significantly better for people who use Twitter for social purposes than for people who use Twitter for informational purpose only.

links and other notes:

• http://doi.acm.org/10.1145/1978942.1978974
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/F3HGGWW5

Shyong (Tony) K. Lam, Anuradha Uduwage, Zhenhua Dong, Shilad Sen, David R. Musicant, Loren Terveen, and John Riedl. 2011. WP:Clubhouse?: An Exploration of Wikipedia’s Gender Imbalance. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym ’11), 1–10. https://doi.org/10.1145/2038558.2038560

abstract:

Wikipedia has rapidly become an invaluable destination for millions of information-seeking users. However, media reports suggest an important challenge: only a small fraction of Wikipedia's legion of volunteer editors are female. In the current work, we present a scientific exploration of the gender imbalance in the English Wikipedia's population of editors. We look at the nature of the imbalance itself, its effects on the quality of the encyclopedia, and several conflict-related factors that may be contributing to the gender gap. Our findings confirm the presence of a large gender gap among editors and a corresponding gender-oriented disparity in the content of Wikipedia's articles. Further, we find evidence hinting at a culture that may be resistant to female participation.

links and other notes:

• http://grouplens.org/system/files/wp-gender-wikisym2011.pdf
• http://dl.acm.org/authorize?N97573
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/QSXNWDVW

Aaron Halfaker, Bryan Song, D. Alex Stuart, Aniket Kittur, and John Riedl. 2011. NICE: Social Translucence Through UI Intervention. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym ’11), 101–104. https://doi.org/10.1145/2038558.2038575

abstract:

Social production systems such as Wikipedia rely on attracting and motivating volunteer contributions to be successful. One strong demotivating factor can be when an editor's work is discarded, or "reverted", by others. In this paper we demonstrate evidence of this effect and design a novel interface aimed at improving communication between the reverting and reverted editors. We deployed the interface in a controlled experiment on the live Wikipedia site, and report on changes in the behavior of 487 contributors who were reverted by editors using our interface. Our results suggest that simple interface modifications (such as informing Wikipedians that the editor they are reverting is a newcomer) can have substantial positive effects in protecting against contribution loss in newcomers and improving the quality of work done by more experienced contributors.

links and other notes:

• http://dl.acm.org/authorize?N97570
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/XIA3JK9S

Mikhil Masli. 2011. Crowdsourcing Maps. Computer 44, 90–93.

abstract:

links and other notes:

• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RP9RKPMR

Xiao Dong, F. Maxwell Harper, and Joseph A. Konstan. 2011. Entity-linking Interfaces in User-contributed Content: Preference and Performance. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’11), 2187–2196. https://doi.org/10.1145/1978942.1979261

abstract:

The ability to embed links to other resources in user generated content can help authors create more useful and usable content. A variety of interfaces have emerged for entity-linking at popular online sites; such interfaces vary in the way that entity linking is initiated (in-band or out-of-band with respect to the message creation), the timing of entity resolution (interrupting or deferred), and the method of resolving the entity (auto-completion or search). Four interfaces mimicking popular entity linking websites were developed and tested. Results showed that out-of-band initiation (e.g., a link button) was faster to learn, but that in-band initiation performance improved with familiarity. Deferred search was disliked and led to worse performance. And auto-completion was generally preferred to search interfaces.

links and other notes:

• http://dl.acm.org/authorize?N97575
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/SNIZ8JM2

S. Andrew Sheppard and Loren Terveen. 2011. Quality is a Verb: The Operationalization of Data Quality in a Citizen Science Community. In Proceedings of the 7th International Symposium on Wikis and Open Collaboration (WikiSym ’11), 29–38. https://doi.org/10.1145/2038558.2038565

abstract:

Citizen science is becoming more valuable as a potential source of environmental data. Involving citizens in data collection has the added educational benefits of increased scientific awareness and local ownership of environmental concerns. However, a common concern among domain experts is the presumed lower quality of data submitted by volunteers. In this paper, we explore data quality assurance practices in River Watch, a community-based monitoring program in the Red River basin. We investigate how the participants in River Watch understand and prioritize data quality concerns. We found that data quality in River Watch is primarily maintained through universal adherence to standard operating procedures, but there remain areas where technological intervention may help. We also found that rigorous data quality assurance practices appear to enhance rather than hinder the educational goals of the program. We draw implications for the design of quality assurance mechanisms for River Watch and other citizen science projects.

links and other notes:

• https://wq.io/research/quality
• http://dl.acm.org/authorize?N97564
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/I4JKNWE7

Reid Priedhorsky. 2010. The value of geographic Wikis. University of Minnesota, Minneapolis, MN. Retrieved April 30, 2015 from http://conservancy.umn.edu/handle/11299/97898

abstract:

This thesis responds to the dual rising trends of geographic content and open content, where the core value of an information system is derived from the work of users. We define the essential properties of an emerging technology, the geographic wiki or geowiki, as well as two variations we invented: the computational geowiki, where user wiki input feeds an algorithm, and the personalized geowiki, where the system provides a personalized interpretation. We focus on two systems to develop these ideas. First, Cyclopath, a research geowiki we founded, serves the bicycle navigation needs of cyclists. We also present analysis in the context of Wikipedia, the well-known and highly successful wiki encyclopedia, using its size and maturity to draw lessons for smaller, younger systems which are far more numerous but hope to grow. We ask three questions with respect to this new technology. First, can it be built? Yes. This thesis describes the design and implementation of Cyclopath, which has grown to be a production system with thousands of users. Second, is it useful? Yes. We identified a representative geographic community, bicyclists, and they both tell us that the information in the Cyclopath geowiki is useful and show us by using the system in great numbers. We also present new ways to measure value in wikis, introducing new techniques for doing so from the perspective of information consumers. In particular, user work in Cyclopath has shortened the average route by 1 km. Also, we present techniques for obtaining more contributions (familiarity matters -- sometimes -- and users do work beyond what they are asked to) and algorithms for increasing the value of geowiki content by personalizing it, showing that traditional rating prediction algorithms (collaborative filtering) are not effective but simple algorithms based on clustering are. Finally, who cares? Many people. There are numerous communities with great interest in geographic information but limited, incomplete, or awkward access because the relevant knowledge is distributed among members of the community and otherwise unavailable. As our results demonstrate, geowikis are an effective way of gathering and disseminating geographic information, more so than previous techniques. Thus, this research has broad value.

links and other notes:

• http://conservancy.umn.edu/handle/11299/97898
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/KDEU8BMT

Jilin Chen and Joseph A. Konstan. 2010. Conference Paper Selectivity and Impact. Commun. ACM 53, 6: 79–83. https://doi.org/10.1145/1743546.1743569

abstract:

Conference acceptance rate signals future impact of published conference papers.

links and other notes:

• http://dl.acm.org/authorize?N97586
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/3REIMK56

Yan Chen, F. Maxwell Harper, Joseph Konstan, and Sherry Xin Li. 2010. Replication data for: Social Comparisons and Contributions to Online Communities: A Field Experiment on MovieLens. https://doi.org/10.3886/E112364V1

abstract:

We design a field experiment to explore the use of social comparison to increase contributions to an online community. We find that, after receiving behavioral information about the median user's total number of movie ratings, users below the median demonstrate a 530 percent increase in the number of monthly movie ratings, while those above the median decrease their ratings by 62 percent. When given outcome information about the average user's net benefit score, above-average users mainly engage in activities that help others. Our findings suggest that effective personalized social information can increase the level of public goods provision. (JEL C93, H41, L82)

links and other notes:

• https://www.openicpsr.org/openicpsr/project/112364/version/V1/view
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FJZZGDXP

Katherine Panciera, Reid Priedhorsky, Thomas Erickson, and Loren Terveen. 2010. Lurking? Cyclopaths?: A Quantitative Lifecycle Analysis of User Behavior in a Geowiki. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10), 1917–1926. https://doi.org/10.1145/1753326.1753615

abstract:

Online communities produce rich behavioral datasets, e.g., Usenet news conversations, Wikipedia edits, and Facebook friend networks. Analysis of such datasets yields important insights (like the "long tail" of user participation) and suggests novel design interventions (like targeting users with personalized opportunities and work requests). However, certain key user data typically are unavailable, specifically viewing, pre-registration, and non-logged-in activity. The absence of data makes some questions hard to answer; ac- cess to it can strengthen, extend, or cast doubt on previous results. We report on analysis of user behavior in Cyclopath, a geographic wiki and route-finder for bicyclists. With access to viewing and non-logged-in activity data, we were able to: (a) replicate and extend prior work on user lifecycles in Wikipedia, (b) bring to light some pre-registration activity, thus testing for the presence of "educational lurking," and (c) demonstrate the locality of geographic activity and how editing and viewing are geographically correlated.

links and other notes:

• http://dl.acm.org/authorize?N97583
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/9QPFRA2N

Reid Priedhorsky, Mikhil Masli, and Loren Terveen. 2010. Eliciting and Focusing Geographic Volunteer Work. In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work (CSCW ’10), 61–70. https://doi.org/10.1145/1718918.1718931

abstract:

Open content communities such as wikis derive their value from the work done by users. However, a key challenge is to elicit work that is sufficient and focused where needed. We address this challenge in a geographic open content community, the Cyclopath bicycle route finding system. We devised two techniques to elicit and focus user work, one using familiarity to direct work opportunities and another visually highlighting them. We conducted a field experiment, finding that (a) the techniques succeeded in eliciting user work, (b) the distribution of work across users was highly unequal, and (c) user work benefitted the community (reducing the length of the average computed route by 1 kilometer).

links and other notes:

• http://dl.acm.org/authorize?N97584
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/FFXVJFRU

Fernando Torre, S. Andrew Sheppard, Reid Priedhorsky, and Loren Terveen. 2010. Bumpy, Caution with Merging: An Exploration of Tagging in a Geowiki. In Proceedings of the 16th ACM International Conference on Supporting Group Work (GROUP ’10), 155–164. https://doi.org/10.1145/1880071.1880097

abstract:

We introduced tags into the Cyclopath geographic wiki for bicyclists. To promote the creation of useful tags, we made tags wiki objects, giving ownership of tag applications to the community, not to individuals. We also introduced a novel interface that lets users fine-tune their routing preferences with tags. We analyzed the Cyclopath tagging vocabulary, the relationship of tags to existing annotation techniques (notes and ratings), and the roles users take on with respect to tagging, notes, and ratings. Our findings are: two distinct tagging vocabularies have emerged, one around each of the two main types of geographic objects in Cyclopath; tags and notes have overlapping content but serve distinct purposes; users employ both ratings and tags to express their route-finding preferences, and use of the two techniques is moderately correlated; and users are highly specialized in their use of tags and notes. These findings suggest new design opportunities, including semi-automated methods to infer new annotations in a geographic context.

links and other notes:

• http://dl.acm.org/authorize?N97587
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/S2S33FPI

Aditya Pal and Joseph A. Konstan. 2010. Expert Identification in Community Question Answering: Exploring Question Selection Bias. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM ’10), 1505–1508. https://doi.org/10.1145/1871437.1871658

abstract:

Community Question Answering (CQA) services enables users to ask and answer questions. In these communities, there are typically a small number of experts amongst the large population of users. We study which questions a user select for answering and show that experts prefer answering questions where they have a higher chance of making a valuable contribution. We term this preferential selection as question selection bias and propose a mathematical model to estimate it. Our results show that using Gaussian classification models we can effectively distinguish experts from ordinary users over their selection biases. In order to estimate these biases, only a small amount of data per user is required, which makes an early identification of expertise a possibility. Further, our study of bias evolution reveals that they do not show significant changes over time indicating that they emanates from the intrinsic characteristics of users.

links and other notes:

• http://dl.acm.org/authorize?62248
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/B9DTIKEC

Shyong K. Lam, Jawed Karim, and John Riedl. 2010. The Effects of Group Composition on Decision Quality in a Social Production Community. In Proceedings of the 16th ACM International Conference on Supporting Group Work (GROUP ’10), 55–64. https://doi.org/10.1145/1880071.1880083

abstract:

Online social production communities allow efficient construction of valuable and high-quality information sources. To be successful, community members must be effective at collaboration, including makink collective decisions in the presence of disagreement. We examined over 100,000 decisions made by small working groups in Wikipedia, and analyzed how decision quality in these online groups is influenced by four group composition factors: the size of the group, how members were invited to the group, the prior experience of group members, and apparent bias shown by the group administrator. Our findings lead us to recommendations for designers of social production communities.

links and other notes:

• http://dl.acm.org/authorize?N97589
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UR9KBAT3

Michael D. Ekstrand, Praveen Kannan, James A. Stemper, John T. Butler, Joseph A. Konstan, and John T. Riedl. 2010. Automatically Building Research Reading Lists. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys ’10), 159–166. https://doi.org/10.1145/1864708.1864740

abstract:

All new researchers face the daunting task of familiarizing themselves with the existing body of research literature in their respective fields. Recommender algorithms could aid in preparing these lists, but most current algorithms do not understand how to rate the importance of a paper within the literature, which might limit their effectiveness in this domain. We explore several methods for augmenting existing collaborative and content-based filtering algorithms with measures of the influence of a paper within the web of citations. We measure influence using well-known algorithms, such as HITS and PageRank, for measuring a node's importance in a graph. Among these augmentation methods is a novel method for using importance scores to influence collaborative filtering. We present a task-centered evaluation, including both an offline analysis and a user study, of the performance of the algorithms. Results from these studies indicate that collaborative filtering outperforms content-based approaches for generating introductory reading lists.

links and other notes:

• http://dl.acm.org/authorize?N97581
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/IGQXFGUK

Jilin Chen, Yuqing Ren, and John Riedl. 2010. The Effects of Diversity on Group Productivity and Member Withdrawal in Online Volunteer Groups. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10), 821–830. https://doi.org/10.1145/1753326.1753447

abstract:

The "wisdom of crowds" argument emphasizes the importance of diversity in online collaborations, such as open source projects and Wikipedia. However, decades of research on diversity in offline work groups have painted an inconclusive picture. On the one hand, the broader range of insights from a diverse group can lead to improved outcomes. On the other hand, individual differences can lead to conflict and diminished performance. In this paper, we examine the effects of group diversity on the amount of work accomplished and on member withdrawal behaviors in the context of WikiProjects. We find that increased diversity in experience with Wikipedia increases group productivity and decreases member withdrawal -- up to a point. Beyond that point, group productivity remains high, but members are more likely to withdraw. Strikingly, no such diminishing returns were observed for differences in member interest, which increases productivity and decreases member withdrawal in a linear fashion. Our results suggest that the low visibility of individual differences in online groups may allow them to harvest more of the benefits of diversity while bearing less of the cost. We discuss how our findings can inform further research of online collaboration.

links and other notes:

• http://dl.acm.org/authorize?N97582
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/8WARBK83

Jesse Vig. 2010. Intelligent Tagging Interfaces: Beyond Folksonomy. In Adjunct Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology (UIST ’10), 371–374. https://doi.org/10.1145/1866218.1866226

abstract:

This paper summarizes our work on using tags to broaden the dialog between a recommender system and its users. We present two tagging applications that enrich this dialog: tagsplanations are tag-based explanations of recommendations provided by a system to its users, and Movie Tuner is a conversational recommender system that enables users to provide feedback on movie recommendations using tags. We discuss the design of both systems and the experimental methodology used to evaluate the design choices.

links and other notes:

• http://doi.acm.org/10.1145/1866218.1866226
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/NN5VCRKR

Jilin Chen, Rowan Nairn, Les Nelson, Michael Bernstein, and Ed Chi. 2010. Short and Tweet: Experiments on Recommending Content from Information Streams. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10), 1185–1194. https://doi.org/10.1145/1753326.1753503

abstract:

More and more web users keep up with newest information through information streams such as the popular micro-blogging website Twitter. In this paper we studied content recommendation on Twitter to better direct user attention. In a modular approach, we explored three separate dimensions in designing such a recommender: content sources, topic interest models for users, and social voting. We implemented 12 recommendation engines in the design space we formulated, and deployed them to a recommender service on the web to gather feedback from real Twitter users. The best performing algorithm improved the percentage of interesting content to 72% from a baseline of 33%. We conclude this work by discussing the implications of our recommender design and how our design can generalize to other information streams.

links and other notes:

• http://doi.acm.org/10.1145/1753326.1753503
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/IAZI9NWG

Shilad Sen Jesse Vig. 2010. Computing the tag genome. University of Minnesota Technical Report. Retrieved from http://www.researchgate.net/publication/228480787_Computing_the_tag_genome

abstract:

Tags help users understand a rich information space, by show- ing them specific text annotations for each item in the space and enabling them to search by these annotations. Often, however, users may wish to move from one item to other items that are similar overall, but that differ in key charac- teristics. For example, a user who loves Pulp Fiction might want to see a similar movie, but might be in a mood for a less “dark” movie. In separate work we introduce Movie Tuner, a novel interface that supports navigation from one item to nearby items along dimensions represented by tags. In the present paper we describe a data structure called the tag genome that enables this form of navigation. The tag genome encodes each item’s relationship to a common set of tags by applying machine learning algorithms to user- contributed content.

links and other notes:

• http://www.grouplens.org/system/files/genome.pdf
• http://www.researchgate.net/publication/228480787_Computing_the_tag_genome
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/TQKSFB7P

Jesse Vig, Matthew Soukup, Shilad Sen, and John Riedl. 2010. Tag Expression: Tagging with Feeling. In Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology (UIST ’10), 323–332. https://doi.org/10.1145/1866029.1866079

abstract:

In this paper we introduce tag expression, a novel form of preference elicitation that combines elements from tagging and rating systems. Tag expression enables users to apply affect to tags to indicate whether the tag describes a reason they like, dislike, or are neutral about a particular item. We present a user interface for applying affect to tags, as well as a technique for visualizing the overall community's affect. By analyzing 27,773 tag expressions from 553 users entered in a 3-month period, we empirically evaluate our design choices. We also present results of a survey of 97 users that explores users' motivations in tagging and measures user satisfaction with tag expression.

links and other notes:

• http://dl.acm.org/authorize?N97580
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/RICPGNB9

Michael S. Bernstein, Bongwon Suh, Lichan Hong, Jilin Chen, Sanjay Kairam, and Ed H. Chi. 2010. Eddi: Interactive Topic-based Browsing of Social Status Streams. In Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology (UIST ’10), 303–312. https://doi.org/10.1145/1866029.1866077

abstract:

Twitter streams are on overload: active users receive hundreds of items per day, and existing interfaces force us to march through a chronologically-ordered morass to find tweets of interest. We present an approach to organizing a user's own feed into coherently clustered trending topics for more directed exploration. Our Twitter client, called Eddi, groups tweets in a user's feed into topics mentioned explicitly or implicitly, which users can then browse for items of interest. To implement this topic clustering, we have developed a novel algorithm for discovering topics in short status updates powered by linguistic syntactic transformation and callouts to a search engine. An algorithm evaluation reveals that search engine callouts outperform other approaches when they employ simple syntactic transformation and backoff strategies. Active Twitter users evaluated Eddi and found it to be a more efficient and enjoyable way to browse an overwhelming status update feed than the standard chronological interface.

links and other notes:

• http://doi.acm.org/10.1145/1866029.1866077
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/UEWVKIHJ

J. Kawale, A. Pal, and J. Srivastava. 2009. Churn Prediction in MMORPGs: A Social Influence Based Approach. In International Conference on Computational Science and Engineering, 2009. CSE ’09, 423–428. https://doi.org/10.1109/CSE.2009.80

abstract:

Massively multiplayer online role playing games (MMORPGs) are computer based games in which players interact with one another in the virtual world. Worldwide revenues for MMORPGs have seen amazing growth in last few years and it is more than a 2 billion dollars industry as per current estimates. Huge amount of revenue potential has attracted several gaming companies to launch online role playing games. One of the major problems these companies suffer apart from fierce competition is erosion of their customer base. Churn is a big problem for the gaming companies as churners impact negatively in the word of-mouth reports for potential and existing customers leading to further erosion of user base.We study the problem of player churn in the popular MMORPG EverQuest II. The problem of churn prediction has been studied extensively in the past in various domains and social network analysis has recently been applied to the problem to understand the effects of the strength of social ties and the structure and dynamics of a social network in churn. In this paper, we propose a churn prediction model based on examining social influence among players and their personal engagement in the game. We hypothesize that social influence is a vector quantity, with components negative influence and positive influence. We propose a modified diffusion model to propagate the influence vector in the players network which represents the social influence on the player from his network. We measure a players personal engagement based on his activity patterns and use it in the modified diffusion model and churn prediction. Our method for churn prediction which combines social influence and player engagement factors has shown to improve prediction accuracy significantly for our dataset as compared to prediction using the conventional diffusion model or the player engagement factor, thus validating our hypothesis that combination of both these factors could lead to a more accurate churn prediction.

links and other notes:

• http://www-users.cs.umn.edu/~apal/res/ChurnPrediction_SocialComp09.pdf
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/7U5BZIR7

Michael Ludwig, Reid Priedhorsky, and Loren Terveen. 2009. Path Selection: A Novel Interaction Technique for Mapping Applications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’09), 2309–2318. https://doi.org/10.1145/1518701.1519055

abstract:

Many online mapping applications let users define routes, perhaps for sharing a favorite bicycle commuting route or rating several contiguous city blocks. At the UI level, defining a route amounts to selecting a fairly large number of objects - the individual segments of roads and trails that make up the route. We present a novel interaction technique for this task called path selection. We implemented the technique and evaluated it experimentally, finding that adding path selection to a state-of-the-art technique for selecting individual objects reduced route definition time by about a factor of 2, reduced errors, and improved user satisfaction. Detailed analysis of the results showed path selection is most advantageous (a) for routes with long straight segments and (b) when objects that are optimal click targets also are visually attractive.

links and other notes:

• http://dl.acm.org/authorize?N97538
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ETNVQ4D7

Shyong (Tony) K. Lam and John Riedl. 2009. Is Wikipedia Growing a Longer Tail? In Proceedings of the ACM 2009 International Conference on Supporting Group Work (GROUP ’09), 105–114. https://doi.org/10.1145/1531674.1531690

abstract:

Wikipedia has millions of articles, many of which receive little attention. One group of Wikipedians believes these obscure entries should be removed because they are uninteresting and neglected; these are the deletionists. Other Wikipedians disagree, arguing that this long tail of articles is precisely Wikipedia's advantage over other encyclopedias; these are the inclusionists. This paper looks at two overarching questions on the debate between deletionists and inclusionists: (1) What are the implications to the long tail of the evolving standards for article birth and death? (2) How is viewership affected by the decreasing notability of articles in the long tail? The answers to five detailed research questions that are inspired by these overarching questions should help better frame this debate and provide insight into how Wikipedia is evolving.

links and other notes:

• http://dl.acm.org/authorize?N97524
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/ND2DC7JE

F. Maxwell Harper, Daniel Moy, and Joseph A. Konstan. 2009. Facts or Friends?: Distinguishing Informational and Conversational Questions in Social Q&A Sites. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’09), 759–768. https://doi.org/10.1145/1518701.1518819

abstract:

Tens of thousands of questions are asked and answered every day on social question and answer (Q&A) Web sites such as Yahoo Answers. While these sites generate an enormous volume of searchable data, the problem of determining which questions and answers are archival quality has grown. One major component of this problem is the prevalence of conversational questions, identified both by Q&A sites and academic literature as questions that are intended simply to start discussion. For example, a conversational question such as "do you believe in evolution?" might successfully engage users in discussion, but probably will not yield a useful web page for users searching for information about evolution. Using data from three popular Q&A sites, we confirm that humans can reliably distinguish between these conversational questions and other informational questions, and present evidence that conversational questions typically have much lower potential archival value than informational questions. Further, we explore the use of machine learning techniques to automatically classify questions as conversational or informational, learning in the process about categorical, linguistic, and social differences between different question types. Our algorithms approach human performance, attaining 89.7% classification accuracy in our experiments.

links and other notes:

• http://dl.acm.org/authorize?62241
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/H2B542WC

Michael D. Ekstrand and John T. Riedl. 2009. Rv You’Re Dumb: Identifying Discarded Work in Wiki Article History. In Proceedings of the 5th International Symposium on Wikis and Open Collaboration (WikiSym ’09), 4:1-4:10. https://doi.org/10.1145/1641309.1641317

abstract:

Wiki systems typically display article history as a linear sequence of revisions in chronological order. This representation hides deeper relationships among the revisions, such as which earlier revision provided most of the content for a later revision, or when a revision effectively reverses the changes made by a prior revision. These relationships are valuable in understanding what happened between editors in conflict over article content. We present methods for detecting when a revision discards the work of one or more other revisions, a means of visualizing these relationships in-line with existing history views, and a computational method for detecting discarded work. We show through a series of examples that these tools can aid mediators of wiki content disputes by making salient the structure of the ongoing conflict. Further, the computational tools provide a means of determining whether or not a revision has been accepted by the community of editors surrounding the article.

links and other notes:

• http://dl.acm.org/authorize?N97522
• zotero: https://www.zotero.org/groups/grouplens/items/itemKey/QUEB793T