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Measuring the Diversity of Facebook Reactions to Research

Authors:

Murtuza ShahzadAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 4, Issue GROUP

Article No.: 12, Pages 1 - 17

https://doi.org/10.1145/3375192

Published: 04 January 2020 Publication History

Abstract

Online and in the real world, communities are bonded together by emotional consensus around core issues. Emotional responses to scientific findings often play a pivotal role in these core issues. When there is too much diversity of opinion on topics of science, emotions flare up and give rise to conflict. This conflict threatens positive outcomes for research. Emotions have the power to shape how people process new information. They can color the public's understanding of science, motivate policy positions, even change lives. And yet little work has been done to evaluate the public's emotional response to science using quantitative methods. In this paper, we use a dataset of responses to scholarly articles on Facebook to analyze the dynamics of emotional valence, intensity, and diversity. We present a novel way of weighting click-based reactions that increases their comprehensibility, and use these weighted reactions to develop new metrics of aggregate emotional responses. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that research articles related to gender, genetics, or agricultural/environmental sciences elicit significantly different emotional responses from users than other research topics. We also find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared---a conclusion that contradicts previous studies of other social media platforms.

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Cited By

Moudene YIdrais JEl Abassi RSabour A(2024)Gender-Based Analysis of User Reactions to Facebook PostsBig Data Mining and Analytics10.26599/BDMA.2023.90200057:1(75-86)Online publication date: Mar-2024
https://doi.org/10.26599/BDMA.2023.9020005
Phillips J(2023)Laughing at death: Facebook, the ‘haha’ reaction, and death coverage on local US news pagesPopular Communication10.1080/15405702.2023.228773822:1(17-32)Online publication date: Dec-2023
https://doi.org/10.1080/15405702.2023.2287738
Shahzad MFreeman CRahimi MAlhoori H(2023)Predicting Facebook sentiments towards researchNatural Language Processing Journal10.1016/j.nlp.2023.1000103(100010)Online publication date: Jun-2023
https://doi.org/10.1016/j.nlp.2023.100010
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Measuring the Diversity of Facebook Reactions to Research

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 4, Issue GROUP

GROUP

January 2020

405 pages

EISSN:2573-0142

DOI:10.1145/3378060

Editors:
Cliff Lampe
University of Michigan
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Jeff Nichols
Google
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Karrie Karahalios
University of Illinois Urbana-Champaign
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Geraldine Fitzpatrick
Vienna University of Technology
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Uichin Lee
KAIST
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Andres Monroy-Hernandez
Microsoft Research
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Wolfgang Stuerzlinger
Simon Fraser University, Canada

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Publication History

Published: 04 January 2020

Published in PACMHCI Volume 4, Issue GROUP

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Cited By

Moudene YIdrais JEl Abassi RSabour A(2024)Gender-Based Analysis of User Reactions to Facebook PostsBig Data Mining and Analytics10.26599/BDMA.2023.90200057:1(75-86)Online publication date: Mar-2024
https://doi.org/10.26599/BDMA.2023.9020005
Phillips J(2023)Laughing at death: Facebook, the ‘haha’ reaction, and death coverage on local US news pagesPopular Communication10.1080/15405702.2023.228773822:1(17-32)Online publication date: Dec-2023
https://doi.org/10.1080/15405702.2023.2287738
Shahzad MFreeman CRahimi MAlhoori H(2023)Predicting Facebook sentiments towards researchNatural Language Processing Journal10.1016/j.nlp.2023.1000103(100010)Online publication date: Jun-2023
https://doi.org/10.1016/j.nlp.2023.100010
Pratama AFirmansyah F(2022) COVID-19 mass media coverage in English and public reactions: a West-East comparison via Facebook posts PeerJ Computer Science10.7717/peerj-cs.11118(e1111)Online publication date: 28-Sep-2022
https://doi.org/10.7717/peerj-cs.1111
Kleiner J(2022)Battling Defense Austerity Cuts With the COVID-19 Crisis Response? The Czech Army’s Online Public PerceptionArmed Forces & Society10.1177/0095327X22111465450:1(202-223)Online publication date: 7-Aug-2022
https://doi.org/10.1177/0095327X221114654
Oliveira LCosta WVaz De Melo PBenevenuto F(2022)How Politicians Communicate in Social Media: A Cross-Platform StudyProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3558232(75-83)Online publication date: 7-Nov-2022
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Weeraprameshwara GJayawickrama Vde Silva NWijeratne Y(2022)Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook DataProceedings of the 2022 3rd International Conference on Artificial Intelligence in Electronics Engineering10.1145/3512826.3512829(16-22)Online publication date: 11-Jan-2022
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Suhaimi SBakar NAzmi N(2022)Malay Sarcasm Detection on Social Media: A Review, Taxonomy, and Future Directions2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA)10.1109/ICITDA55840.2022.9971158(1-8)Online publication date: 4-Nov-2022
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Bagić Babac M(2022)Emotion analysis of user reactions to online newsInformation Discovery and Delivery10.1108/IDD-04-2022-002751:2(179-193)Online publication date: 14-Nov-2022
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Oliveira LAzevedo J(2022)Using Social Media Categorical Reactions as a Gateway to Identify Hate Speech in COVID-19 NewsSN Computer Science10.1007/s42979-022-01421-54:1Online publication date: 16-Oct-2022
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