People think that social media platforms do (but should not) amplify divisive content
Abstract
Recent studies have documented the type of content that is most likely to spread widely, or go “viral,” on social media, yet little is known about people’s perceptions of what goes viral or what should go viral. This is critical to understand because there is widespread debate about how to improve or regulate social media algorithms. We recruited a sample of participants that is nationally representative of the U.S. population (according to age, gender, and race/ethnicity) and surveyed them about their perceptions of social media virality ( n = 511). In line with prior research, people believe that divisive content, moral outrage, negative content, high-arousal content, and misinformation are all likely to go viral online. However, they reported that this type of content should not go viral on social media. Instead, people reported that many forms of positive content—such as accurate content, nuanced content, and educational content—are not likely to go viral even though they think this content should go viral. These perceptions were shared among most participants and were only weakly related to political orientation, social media usage, and demographic variables. In sum, there is broad consensus around the type of content people think social media platforms should and should not amplify, which can help inform solutions for improving social media.
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U.S. adults believe social media platforms amplify negative, emotionally-charged, and out-group hostile content but should not.
“Rathje et al. [42] found that U.S. adults believe social media platforms amplify negative, emotionally-charged, and out-group hostile content but should not.”
From Twitter's Algorithm: Amplifying Anger, Animosity, and Affective Polarization (2023) · cites this paper as [42]
This strategy places a premium on user attention, which has led companies to deploy AI and machine learning techniques to prolong user engagement, often to the detriment of individual and societal well-being.
“This strategy places a premium on user attention, which has led companies to deploy AI and machine learning techniques to prolong user engagement, often to the detriment of individual and societal well-being (12–15).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [15]
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BackgroundAlgorithm-mediated social learning in online social networks
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- Authors
- Rathje, S., Robertson, C., Brady, W. J., & Van Bavel, J. J.
- Affiliations
- New York University (US)
- Northwestern University (US)
- Department of Psychology & Center for Neural Science, New York University
- Venue
- Perspectives on Psychological Science
- Publisher
- SAGE Publications
- Published
- 2023-09-26
- Volume
- 19
- Issue
- 5
- Pages
- 781-795
- ISSN
- 1745-6916 (ISSN-L)
- 1745-6924
- OpenAlex
- W4387037812
- Semantic Scholar
- 31d7a4f1aec27a07d89634c9a151d5a5dc4dcd1b
- Cited by
- 59
- References
- 87
- Citations by year
- 2026: 18 · 2025: 20 · 2024: 14 · 2023: 7
- FWCI
- 25.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Perspectives on Psychological Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Russell Sage foundation (2110-33990)
- John Templeton Foundation (61378)
- Gates Cambridge Scholarship (OPP1144)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Communication › Communication and COVID-19 Impact
- Keywords
- social media virality, moral outrage, misinformation, content amplification, social media regulation, algorithmic bias, political orientation, social media use, public opinion
- MeSH terms
- Adolescent, Adult, Female, Humans, Male, Middle Aged, United States, Young Adult, Social Media
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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