abstract

Topicality, time, and sentiment in online news comments

Authors:

Nicholas Diakopoulos,

Mor NaamanAuthors Info & Claims

CHI EA '11: CHI '11 Extended Abstracts on Human Factors in Computing Systems

Pages 1405 - 1410

https://doi.org/10.1145/1979742.1979782

Published: 07 May 2011 Publication History

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Abstract

In this paper we examine the relationships between news comment topicality, temporality, sentiment, and quality in a dataset of 54,540 news comments. Initial observations indicate that comment sentiments, both positive and negative, can be useful indicators of discourse quality, and that aggregate temporal patterns in positive sentiment exist on comment threads.

References

[1]

Burke, M. and Kraut, R., Mind Your Ps and Qs: The Impact of Politeness and Rudeness in Online Communities. Computer Supported Cooperative Work (CSCW), (2008).

Digital Library

Google Scholar

[2]

Chmiel, A., Sienkiewicz, J., Paltoglou, G., Buckley, K., Thelwall, M. and Holyst, J. Negative Emtions Boost Users Activity at BBC Forum. 2010. http://arxiv.org/abs/1011.545.

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[3]

Diakopoulos, N. and Naaman, M., Towards Quality Discourse in Online News Comments. Proc. CSCW, (2011).

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Google Scholar

[4]

Riloff, E. and Wiebe, J., Learning Extraction Patterns for Subjective Expressions. Empirical Methods in Natural Language Processing (EMNLP), (2003).

Digital Library

Google Scholar

[5]

Sood, S.O. and Churchill, E.F., Anger Management: Using Sentiment Analysis to Manage Online Communities. Grace Hopper Celebration, (2010).

Google Scholar

Cited By

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Alafwan BSiallagan MPutro U(2023)Comments Analysis on Social Media: A ReviewICST Transactions on Scalable Information Systems10.4108/eetsis.3843Online publication date: 6-Sep-2023
https://doi.org/10.4108/eetsis.3843
Song JHe YLi R(2023)Research on the Application of Machine Learning in the Field of News Commentary Analysis2023 International Conference on Culture-Oriented Science and Technology (CoST)10.1109/CoST60524.2023.00031(110-115)Online publication date: 11-Oct-2023
https://doi.org/10.1109/CoST60524.2023.00031
McInnis BAjmani LDow S(2022)Engagement or Knowledge Retention: Exploring Trade-offs in Promoting Discussion at News WebsitesProceedings of the ACM on Human-Computer Interaction10.1145/35551946:CSCW2(1-38)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555194
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Index Terms

Topicality, time, and sentiment in online news comments
1. Human-centered computing
  1. Collaborative and social computing

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Information & Contributors

Information

Published In

CHI EA '11: CHI '11 Extended Abstracts on Human Factors in Computing Systems

May 2011

2554 pages

ISBN:9781450302685

DOI:10.1145/1979742

General Chair:
Desney Tan
Microsoft Research
,
Program Chairs:
Bo Begole
PARC
,
Wendy A. Kellogg
IBM Research

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 May 2011

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CHI '11

Sponsor:

SIGCHI

CHI '11: CHI Conference on Human Factors in Computing Systems

May 7 - 12, 2011

BC, Vancouver, Canada

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Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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CHI '25

Sponsor:
sigchi

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April 26 - May 1, 2025

Yokohama , Japan

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Cited By

View all

Alafwan BSiallagan MPutro U(2023)Comments Analysis on Social Media: A ReviewICST Transactions on Scalable Information Systems10.4108/eetsis.3843Online publication date: 6-Sep-2023
https://doi.org/10.4108/eetsis.3843
Song JHe YLi R(2023)Research on the Application of Machine Learning in the Field of News Commentary Analysis2023 International Conference on Culture-Oriented Science and Technology (CoST)10.1109/CoST60524.2023.00031(110-115)Online publication date: 11-Oct-2023
https://doi.org/10.1109/CoST60524.2023.00031
McInnis BAjmani LDow S(2022)Engagement or Knowledge Retention: Exploring Trade-offs in Promoting Discussion at News WebsitesProceedings of the ACM on Human-Computer Interaction10.1145/35551946:CSCW2(1-38)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555194
Naab TKüchler C(2022)Content Analysis in the Research Field of Online User CommentsStandardisierte Inhaltsanalyse in der Kommunikationswissenschaft – Standardized Content Analysis in Communication Research10.1007/978-3-658-36179-2_37(441-450)Online publication date: 25-Sep-2022
https://doi.org/10.1007/978-3-658-36179-2_37
Hwang SFoote J(2021)Why do People Participate in Small Online Communities?Proceedings of the ACM on Human-Computer Interaction10.1145/34796065:CSCW2(1-25)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3479606
Sarkar PPillai J(2021)Approaches for Designing Handheld Augmented Reality Learning Experiences for Mathematics ClassroomsProceedings of the ACM on Human-Computer Interaction10.1145/34796055:CSCW2(1-25)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3479605
Namara MKnijnenburg B(2021)The Differential Effect of Privacy-Related Trust on Groupware Application Adoption and Use during the COVID-19 pandemicProceedings of the ACM on Human-Computer Interaction10.1145/34795495:CSCW2(1-34)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3479549
Doerfler PForte ADe Cristofaro EStringhini GBlackburn JMcCoy D(2021)"I'm a Professor, which isn't usually a dangerous job": Internet-facilitated Harassment and Its Impact on ResearchersProceedings of the ACM on Human-Computer Interaction10.1145/34760825:CSCW2(1-32)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3476082
McInnis BAjmani LSun LHou YZeng ZDow S(2021)Reporting the Community Beat: Practices for Moderating Online Discussion at a News WebsiteProceedings of the ACM on Human-Computer Interaction10.1145/34760745:CSCW2(1-25)Online publication date: 18-Oct-2021
https://dl.acm.org/doi/10.1145/3476074
Ranathunga SLiyanage I(2021)Sentiment Analysis of Sinhala News CommentsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/344503520:4(1-23)Online publication date: 26-May-2021
https://dl.acm.org/doi/10.1145/3445035
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