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Towards Automated Detection of Risky Images Shared by Youth on Social Media

Authors:

Ashwaq Alsoubai,

Gianluca Stringhini,

Pamela WisniewskiAuthors Info & Claims

WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

Pages 1348 - 1357

https://doi.org/10.1145/3543873.3587607

Published: 30 April 2023 Publication History

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Abstract

With the growing ubiquity of the Internet and access to media-based social media platforms, the risks associated with media content sharing on social media and the need for safety measures against such risks have grown paramount. At the same time, risk is highly contextualized, especially when it comes to media content youth share privately on social media. In this work, we conducted qualitative content analyses on risky media content flagged by youth participants and research assistants of similar ages to explore contextual dimensions of youth online risks. The contextual risk dimensions were then used to inform semi- and self-supervised state-of-the-art vision transformers to automate the process of identifying risky images shared by youth. We found that vision transformers are capable of learning complex image features for use in automated risk detection and classification. The results of our study serve as a foundation for designing contextualized and youth-centered machine-learning methods for automated online risk detection.

References

[1]

Shiza Ali, Afsaneh Razi, Kim Seunghyun, Ashwaq Alsoubai, Joshua Gracie, Munmun De Choudhury, Pamela J Wisniewski, and Gianluca Stringhini. 2022. Understanding the Digital Lives of Youth: Analyzing Media Shared within Safe Versus Unsafe Private Conversations on Instagram. (2022).

[2]

Monica Anderson and Jingjing Jiang. 2018. Teens, Social Media and Technology 2018. https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/

[3]

Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. 2021. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 6836–6846.

[4]

Karla Badillo-Urquiola, Zachary Shea, Zainab Agha, Irina Lediaeva, and Pamela Wisniewski. 2021. Conducting risky research with teens: co-designing for the ethical treatment and protection of adolescents. Proceedings of the ACM on Human-Computer Interaction 4, CSCW3 (2021), 1–46.

Digital Library

[5]

Fanni Bányai, Ágnes Zsila, Orsolya Király, Aniko Maraz, Zsuzsanna Elekes, Mark D Griffiths, Cecilie Schou Andreassen, and Zsolt Demetrovics. 2017. Problematic social media use: Results from a large-scale nationally representative adolescent sample. PloS one 12, 1 (2017), e0169839.

[6]

Gedas Bertasius, Heng Wang, and Lorenzo Torresani. 2021. Is space-time attention all you need for video understanding?. In ICML, Vol. 2. 4.

[7]

Rubel Biswas, Victor González-Castro, E Fidalgo, and Deisy Chaves. 2019. Boosting child abuse victim identification in Forensic Tools with hashing techniques. V Jornadas Nacionales de Investigación en Ciberseguridad 1 (2019), 344–345.

[8]

Virginia Braun and Victoria Clarke. 2012. Thematic analysis.American Psychological Association.

[9]

Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. 2020. End-to-end object detection with transformers. In European conference on computer vision. Springer, 213–229.

Digital Library

[10]

Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9650–9660.

[11]

Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.

[12]

Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. 2020. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020).

[13]

Arijit Ghosh Chowdhury, Ramit Sawhney, Puneet Mathur, Debanjan Mahata, and Rajiv Ratn Shah. 2019. Speak up, fight back! detection of social media disclosures of sexual harassment. In Proceedings of the 2019 conference of the North American chapter of the Association for Computational Linguistics: Student research workshop. 136–146.

[14]

Jody Clay-Warner. 2003. The context of sexual violence: Situational predictors of self-protective actions. Violence and victims 18, 5 (2003), 543–556.

[15]

Patricia De Santisteban and Manuel Gámez-Guadix. 2018. Prevalence and risk factors among minors for online sexual solicitations and interactions with adults. The Journal of Sex Research 55, 7 (2018), 939–950.

[16]

Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).

[17]

James W Drisko and Tina Maschi. 2016. Content analysis. Pocket Guide to Social Work Re.

[18]

Sindhu Kiranmai Ernala, Michael L Birnbaum, Kristin A Candan, Asra F Rizvi, William A Sterling, John M Kane, and Munmun De Choudhury. 2019. Methodological gaps in predicting mental health states from social media: triangulating diagnostic signals. In Proceedings of the 2019 chi conference on human factors in computing systems. 1–16.

Digital Library

[19]

facebookresearch/dino. 2022. facebookresearch/dino. https://github.com/facebookresearch/dino

[20]

David Finkelhor, Kerryann Walsh, Lisa Jones, Kimberly Mitchell, and Anne Collier. 2021. Youth internet safety education: Aligning programs with the evidence base. Trauma, violence, & abuse 22, 5 (2021), 1233–1247.

[21]

Abhishek Gangwar, Eduardo Fidalgo, Enrique Alegre, and Víctor González-Castro. 2017. Pornography and child sexual abuse detection in image and video: A comparative evaluation. (2017).

[22]

Abhishek Gangwar, Víctor González-Castro, Enrique Alegre, and Eduardo Fidalgo. 2021. AttM-CNN: Attention and metric learning based CNN for pornography, age and Child Sexual Abuse (CSA) Detection in images. Neurocomputing 445 (2021), 81–104.

[23]

Christine Greenhow. 2011. Youth, learning, and social media. Journal of Educational Computing Research 45, 2 (2011), 139–146.

[24]

Joel W Grube and Elizabeth Waiters. 2005. Alcohol in the media: content and effects on drinking beliefs and behaviors among youth. Adolescent medicine clinics 16, 2 (2005), 327.

[25]

Naeemul Hassan, Amrit Poudel, Jason Hale, Claire Hubacek, Khandaker Tasnim Huq, Shubhra Kanti Karmaker Santu, and Syed Ishtiaque Ahmed. 2020. Towards automated sexual violence report tracking. In Proceedings of the international AAAI conference on web and social media, Vol. 14. 250–259.

[26]

Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 9729–9738.

[27]

Homa Hosseinmardi, Sabrina Arredondo Mattson, Rahat Ibn Rafiq, Richard Han, Qin Lv, and Shivakant Mishra. 2015. Analyzing labeled cyberbullying incidents on the instagram social network. In International conference on social informatics. Springer, 49–66.

[28]

ImageNet. 2021. ImageNet. https://www.image-net.org/

[29]

Instagram. 2022. Abuse and Spam. https://help.instagram.com/165828726894770

[30]

Instagram. 2022. How do I send a message to someone on Instagram?https://help.instagram.com/155540431448273

[31]

Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira. 2021. Perceiver: General perception with iterative attention. In International conference on machine learning. PMLR, 4651–4664.

[32]

Lisa M Jones, Kimberly J Mitchell, and David Finkelhor. 2012. Trends in youth internet victimization: Findings from three youth internet safety surveys 2000–2010. Journal of adolescent Health 50, 2 (2012), 179–186.

[33]

Aparup Khatua, Erik Cambria, and Apalak Khatua. 2018. Sounds of silence breakers: Exploring sexual violence on twitter. In 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, 397–400.

[34]

Seunghyun Kim, Afsaneh Razi, Gianluca Stringhini, Pamela J Wisniewski, and Munmun De Choudhury. 2021. A Human-Centered Systematic Literature Review of Cyberbullying Detection Algorithms. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–34.

Digital Library

[35]

Robin M Kowalski, Susan P Limber, and Annie McCord. 2019. A developmental approach to cyberbullying: Prevalence and protective factors. Aggression and Violent Behavior 45 (2019), 20–32.

[36]

Dong-Hyun Lee 2013. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on challenges in representation learning, ICML, Vol. 3. 896.

[37]

Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 10012–10022.

[38]

Megan K Maas, Kyla M Cary, Elizabeth M Clancy, Bianca Klettke, Heather L McCauley, and Jeff R Temple. 2021. Slutpage use among US college students: the secret and social platforms of image-based sexual abuse. Archives of sexual behavior 50, 5 (2021), 2203–2214.

[39]

Joao Macedo, Filipe Costa, and Jefersson A dos Santos. 2018. A benchmark methodology for child pornography detection. In 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI). IEEE, 455–462.

[40]

Sheri Madigan, Vanessa Villani, Corry Azzopardi, Danae Laut, Tanya Smith, Jeff R Temple, Dillon Browne, and Gina Dimitropoulos. 2018. The prevalence of unwanted online sexual exposure and solicitation among youth: A meta-analysis. Journal of Adolescent Health 63, 2 (2018), 133–141.

[41]

Rose Maghsoudi, Jennifer Shapka, and Pamela Wisniewski. 2020. Examining how online risk exposure and online social capital influence adolescent psychological stress. Computers in Human Behavior 113 (2020), 106488. https://doi.org/10.1016/j.chb.2020.106488

[42]

Felix Mayer and Martin Steinebach. 2017. Forensic image inspection assisted by deep learning. In Proceedings of the 12th International Conference on Availability, Reliability and Security. 1–9.

Digital Library

[43]

Ersilia Menesini, Annalaura Nocentini, and Pamela Calussi. 2011. The measurement of cyberbullying: Dimensional structure and relative item severity and discrimination. Cyberpsychology, behavior, and social networking 14, 5 (2011), 267–274.

[44]

Kimberly J Mitchell, David Finkelhor, and Janis Wolak. 2007. Youth Internet users at risk for the most serious online sexual solicitations. American Journal of Preventive Medicine 32, 6 (2007), 532–537.

[45]

Subhabrata Mukherjee and Ahmed Awadallah. 2020. Uncertainty-aware self-training for few-shot text classification. Advances in Neural Information Processing Systems 33 (2020), 21199–21212.

[46]

Fudong Nian, Teng Li, Yan Wang, Mingliang Xu, and Jun Wu. 2016. Pornographic image detection utilizing deep convolutional neural networks. Neurocomputing 210 (2016), 283–293.

Digital Library

[47]

Gwenn Schurgin O’Keeffe, Kathleen Clarke-Pearson, Council on Communications, and Media. 2011. The impact of social media on children, adolescents, and families. Pediatrics 127, 4 (2011), 800–804.

[48]

Claudia Peersman, Christian Schulze, Awais Rashid, Margaret Brennan, and Carl Fischer. 2016. iCOP: Live forensics to reveal previously unknown criminal media on P2P networks. Digital Investigation 18 (2016), 50–64.

Digital Library

[49]

Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, and Jon Shlens. 2019. Stand-alone self-attention in vision models. Advances in Neural Information Processing Systems 32 (2019).

[50]

Afsaneh Razi, Seunghyun Kim, Ashwaq Alsoubai, Gianluca Stringhini, Thamar Solorio, Munmun De Choudhury, and Pamela J Wisniewski. 2021. A Human-Centered Systematic Literature Review of the Computational Approaches for Online Sexual Risk Detection. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–38.

Digital Library

[51]

Andrew G Reece and Christopher M Danforth. 2017. Instagram photos reveal predictive markers of depression. EPJ Data Science 6 (2017), 1–12.

[52]

Lauren A Reed, Margaret P Boyer, Haley Meskunas, Richard M Tolman, and L Monique Ward. 2020. How do adolescents experience sexting in dating relationships? Motivations to sext and responses to sexting requests from dating partners. Children and Youth Services Review 109 (2020), 104696.

[53]

Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, and Mubarak Shah. 2021. In defense of pseudo-labeling: An uncertainty-aware pseudo-label selection framework for semi-supervised learning. arXiv preprint arXiv:2101.06329 (2021).

[54]

Tara L Rutkowski, Heidi Hartikainen, Kirsten E Richards, and Pamela J Wisniewski. 2021. Family Communication: Examining the Differing Perceptions of Parents and Teens Regarding Online Safety Communication. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2 (2021), 1–23.

Digital Library

[55]

Sarita Schoenebeck, Carol F. Scott, Emma Grace Hurley, Tammy Chang, and Ellen Selkie. 2021. Youth Trust in Social Media Companies and Expectations of Justice: Accountability and Repair After Online Harassment. Proc. ACM Hum.-Comput. Interact. 5, CSCW1, Article 2 (apr 2021), 18 pages. https://doi.org/10.1145/3449076

Digital Library

[56]

Weiwei Shi, Yihong Gong, Chris Ding, Zhiheng MaXiaoyu Tao, and Nanning Zheng. 2018. Transductive semi-supervised deep learning using min-max features. In Proceedings of the European Conference on Computer Vision (ECCV). 299–315.

Digital Library

[57]

Thiago H Silva, Pedro OS Vaz De Melo, Jussara M Almeida, Juliana Salles, and Antonio AF Loureiro. 2013. A picture of Instagram is worth more than a thousand words: Workload characterization and application. In 2013 IEEE International Conference on Distributed Computing in Sensor Systems. IEEE, 123–132.

Digital Library

[58]

Vivek K Singh, Souvick Ghosh, and Christin Jose. 2017. Toward multimodal cyberbullying detection. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 2090–2099.

Digital Library

[59]

Devin Soni and Vivek K Singh. 2018. See no evil, hear no evil: Audio-visual-textual cyberbullying detection. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1–26.

Digital Library

[60]

Catherine Cheng Stahl and Ioana Literat. 2022. # GenZ on TikTok: the collective online self-Portrait of the social media generation. Journal of Youth Studies (2022), 1–22.

[61]

Kurt Thomas, Devdatta Akhawe, Michael Bailey, Dan Boneh, Elie Bursztein, Sunny Consolvo, Nicola Dell, Zakir Durumeric, Patrick Gage Kelley, Deepak Kumar, 2021. Sok: Hate, harassment, and the changing landscape of online abuse. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 247–267.

[62]

Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. 2021. Training data-efficient image transformers & distillation through attention. In International Conference on Machine Learning. PMLR, 10347–10357.

[63]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017).

[64]

Nishant Vishwamitra, Hongxin Hu, Feng Luo, and Long Cheng. 2021. Towards understanding and detecting cyberbullying in real-world images. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA).

[65]

Emily A Vogels. 2022. Teens and cyberbullying 2022. https://www.pewresearch.org/internet/2022/12/15/teens-and-cyberbullying-2022/

[66]

Emily A Vogels, Risa Gelles-Watnick, and Navid Massarat. 2022. Teens, social media and technology 2022. https://www.pewresearch.org/internet/2022/08/10/teens-social-media-and-technology-2022/

[67]

Kate Walker and Emma Sleath. 2017. A systematic review of the current knowledge regarding revenge pornography and non-consensual sharing of sexually explicit media. Aggression and violent behavior 36 (2017), 9–24.

[68]

Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. 2021. Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 568–578.

[69]

Yilin Wang and Baoxin Li. 2015. Sentiment analysis for social media images. In 2015 IEEE international conference on data mining workshop (ICDMW). IEEE, 1584–1591.

Digital Library

[70]

Pamela Wisniewski, Heng Xu, Mary Beth Rosson, Daniel F Perkins, and John M Carroll. 2016. Dear diary: Teens reflect on their weekly online risk experiences. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. 3919–3930.

Digital Library

[71]

Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. 2018. Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3733–3742.

[72]

Emilios Yiallourou, Rafaella Demetriou, and Andreas Lanitis. 2017. On the detection of images containing child-pornographic material. In 2017 24th International Conference on Telecommunications (ICT). IEEE, 1–5.

[73]

Lequan Yu, Shujun Wang, Xiaomeng Li, Chi-Wing Fu, and Pheng-Ann Heng. 2019. Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 605–613.

Digital Library

[74]

Michele Zappavigna. 2016. Social media photography: construing subjectivity in Instagram images. Visual Communication 15, 3 (2016), 271–292.

[75]

Zhedong Zheng and Yi Yang. 2021. Rectifying pseudo label learning via uncertainty estimation for domain adaptive semantic segmentation. International Journal of Computer Vision 129, 4 (2021), 1106–1120.

Digital Library

[76]

Haoti Zhong, Hao Li, Anna Cinzia Squicciarini, Sarah Michele Rajtmajer, Christopher Griffin, David J Miller, and Cornelia Caragea. 2016. Content-Driven Detection of Cyberbullying on the Instagram Social Network. In IJCAI, Vol. 16. 3952–3958.

[77]

Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. 2020. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020).

Cited By

Agha ZAli NPark JWisniewski P(2025)A systematic review on design-based nudges for adolescent online safetyInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2024.10070243(100702)Online publication date: Mar-2025
https://doi.org/10.1016/j.ijcci.2024.100702
Park JWisniewski PSingh V(2024)Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data AnnotationSSRN Electronic Journal10.2139/ssrn.4774771Online publication date: 2024
https://doi.org/10.2139/ssrn.4774771
Alluhidan AAkter MAlsoubai APark JWisniewski P(2024)Teen Talk: The Good, the Bad, and the Neutral of Adolescent Social Media UseProceedings of the ACM on Human-Computer Interaction10.1145/36869618:CSCW2(1-35)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686961
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Index Terms

Towards Automated Detection of Risky Images Shared by Youth on Social Media
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
2. Security and privacy
  1. Human and societal aspects of security and privacy

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WWW '23 Companion: Companion Proceedings of the ACM Web Conference 2023

April 2023

1567 pages

ISBN:9781450394192

DOI:10.1145/3543873

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Published: 30 April 2023

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Cited By

Agha ZAli NPark JWisniewski P(2025)A systematic review on design-based nudges for adolescent online safetyInternational Journal of Child-Computer Interaction10.1016/j.ijcci.2024.10070243(100702)Online publication date: Mar-2025
https://doi.org/10.1016/j.ijcci.2024.100702
Park JWisniewski PSingh V(2024)Leveraging Large Language Models (LLMs) to Support Collaborative Human-AI Online Risk Data AnnotationSSRN Electronic Journal10.2139/ssrn.4774771Online publication date: 2024
https://doi.org/10.2139/ssrn.4774771
Alluhidan AAkter MAlsoubai APark JWisniewski P(2024)Teen Talk: The Good, the Bad, and the Neutral of Adolescent Social Media UseProceedings of the ACM on Human-Computer Interaction10.1145/36869618:CSCW2(1-35)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3686961
Park JGracie JAlsoubai ARazi AWisniewski P(2024)Personally Targeted Risk vs. Humor: How Online Risk Perceptions of Youth vs. Third-Party Annotators Differ based on Privately Shared Media on InstagramProceedings of the 23rd Annual ACM Interaction Design and Children Conference10.1145/3628516.3655799(1-13)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3628516.3655799
Qadir SNiser ACaddle XAlsoubai APark JWisniewski P(2024)Towards a Safer Digital Future: Exploring Stakeholder Perspectives on Creating a Sustainable Youth Online Safety CommunityExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651019(1-10)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651019
Ali NAhn TAgha ZPark JWisniewski P(2024)From Ideas to Impact: Cracking the Code for Effectively Engaging Teens in Long-Term Online Safety ResearchExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650765(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650765
Alsoubai APark JQadir SStringhini GRazi AWisniewski P(2024)Systemization of Knowledge (SoK): Creating a Research Agenda for Human-Centered Real-Time Risk Detection on Social Media PlatformsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642315(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642315
Agha ZPark JWan RAli NWang YDifranzo DBadillo-Urquiola KWisniewski P(2024)Tricky vs. Transparent: Towards an Ecologically Valid and Safe Approach for Evaluating Online Safety Nudges for TeensProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642313(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642313
Caddle XPark JWisniewski P(2024)A Stakeholders’ Analysis of the Sociotechnical Approaches for Protecting Youth OnlineAdvances in Information and Communication10.1007/978-3-031-54053-0_40(587-616)Online publication date: 17-Mar-2024
https://doi.org/10.1007/978-3-031-54053-0_40
Park JAkter MAli NAgha ZAlsoubai AWisniewski P(2023)Towards Resilience and Autonomy-Based Approaches for Adolescents Online SafetySSRN Electronic Journal10.2139/ssrn.4608406Online publication date: 2023
https://doi.org/10.2139/ssrn.4608406
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