Measuring the diversity of Facebook reactions to research

Author ORCID Identifier

Hamed Alhoori: https://orcid.org/0000-0002-4733-6586

Publication Title

Proceedings of the ACM on Human-Computer Interaction

E-ISSN

25730142

Document Type

Article

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.

Publication Date

1-4-2020

DOI

10.1145/3375192

Keywords

Altmetrics, Click-based reactions, Emotion detection, Emotions, Facebook reactions, Social computing, Social media, Text analytics, Web mining

Department

Department of Computer Science

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