Measuring the Diversity of Facebook Reactions to Research
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Abstract (arXiv)
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.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jan 2020.
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Citing papers per year: 2022: 5; 2023: 0; 2024: 0; 2025: 2.
7 papers.
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- 2022Quantifying the Online Long-Term Interest in Researchcited by 1
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- 2025LimTopic: LLM-based Topic Modeling and Text Summarization for Analyzing Scientific Articles limitations8 Mar 2025
- 2025Quantifying the Relevance of Youth Research Cited in the US Policy Documents6 Mar 2025
- 2022Personalized Reward Learning with Interaction-Grounded Learning (IGL)28 Nov 2022
- 2022Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages26 Oct 2022
- 2022Quantifying the Online Long-Term Interest in Research13 Sep 2022
- 2022YouTube and Science: Models for Research Impact1 Sep 2022
- 2022Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data11 Jan 2022