Reshares on social media amplify political news but do not detectably affect beliefs or opinions
Open accessAbstract
We studied the effects of exposure to reshared content on Facebook during the 2020 US election by assigning a random set of consenting, US-based users to feeds that did not contain any reshares over a 3-month period. We find that removing reshared content substantially decreases the amount of political news, including content from untrustworthy sources, to which users are exposed; decreases overall clicks and reactions; and reduces partisan news clicks. Further, we observe that removing reshared content produces clear decreases in news knowledge within the sample, although there is some uncertainty about how this would generalize to all users. Contrary to expectations, the treatment does not significantly affect political polarization or any measure of individual-level political attitudes.
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Other research has failed to find long-term changes in user beliefs due to social media ranking algorithms.
“On the other hand, other research has failed to find long-term changes in user beliefs due to social media ranking algorithms [26,27,29,38]”
From Twitter's Algorithm: Amplifying Anger, Animosity, and Affective Polarization (2023) · cites this paper as [26]
The results section reports no statistically distinguishable effects on several political beliefs and attitudes when reshared content was removed from Facebook feeds. But the intervention removed reshares rather than changing a ranking algorithm, and it ran for three months during the 2020 election; the paper does not establish that beliefs remained unchanged in the long term. (LLM, conf=0.87)
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Cites
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · see citing works
What Makes Online Content Viral?
Berger, J., & Milkman, K. L. (2012) · Journal of Marketing Research · doi:10.1509/jmr.10.0353 · Cited by 3,178 · see citing works
Anderson, M. L. (2008) · Journal of the American Statistical Association · doi:10.1198/016214508000000841 · Cited by 2,410 · see citing works
Exposure to opposing views on social media can increase political polarization
Bail, C. A., Argyle, L. P., Brown, T. W., Bumpus, J. P., Chen, H., Hunzaker, M. B. F., Lee, J., Mann, M., Merhout, F., & Volfovsky, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804840115 · Cited by 1,863 · see citing works
Self-citationEmotion shapes the diffusion of moralized content in social networks
Brady, W. J., Wills, J. A., Jost, J. T., Tucker, J. A., & Van Bavel, J. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1618923114 · Cited by 1,396 · see citing works
Self-citationShifting attention to accuracy can reduce misinformation online
Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021) · Nature · doi:10.1038/s41586-021-03344-2 · Cited by 1,155 · see citing works
Fighting misinformation on social media using crowdsourced judgments of news source quality
Pennycook, G., & Rand, D. G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1806781116 · Cited by 947 · see citing works
The welfare effects of social media
Allcott, H., Braghieri, L., Eichmeyer, S., & Gentzkow, M. (2020) · American Economic Review · doi:10.1257/aer.20190658 · Cited by 857 · see citing works
Self-citationAre people incidentally exposed to news on social media? A comparative analysis
Fletcher, R., & Nielsen, R. K. (2018) · New Media & Society · doi:10.1177/1461444817724170 · Cited by 738 · see citing works
Political News in the News Feed: Learning Politics from Social Media
Bode, L. (2016) · Mass Communication and Society · doi:10.1080/15205436.2015.1045149 · Cited by 545 · see citing works
All the news That’s fit to click: the economics of clickbait media
Munger, K. (2020) · Political Communication · doi:10.1080/10584609.2019.1687626 · Cited by 175 · see citing works
Toward a Description of African American Vernacular English Dialect Regions Using “Black Twitter”
Jones, T. (2015) · American Speech · doi:10.1215/00031283-3442117 · Cited by 144 · see citing works
Wojcieszak, M., Casas, A., Yu, X., Nagler, J., & Tucker, J. A. (2022) · Science Advances · doi:10.1126/sciadv.abn9418 · Cited by 90 · see citing works
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Research works printed but not linked: 11
- E. Bakshy, I. Rosenn, C. Marlow, L. Adamic, in Proceedings of the 21st International Conference on World Wide Web (2012), pp. 519-528.
- J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, J. Leskovec, in Proceedings of the 23rd International Conference on World Wide Web (2014), pp. 925-936.
- J. Ugander, B. Karrer, L. Backstrom, C. Marlow, The Anatomy of the Facebook Social Graph. arXiv:1111.4503 [cs.SI] (2011). · arXiv
- A. Friggeri, L. Adamic, D. Eckles, J. Cheng, in Proceedings of the International AAAI Conference on Web and Social Media (2014), vol. 8, pp. 101-110.
- W. J. Brady, J. A. Wills, J. T. Jost, J. A. Tucker, J. J. Van Bavel, Proc. Natl. Acad. Sci. U.S.A. 114, 7313-7318 (2017).
- R. Levy, Am. Econ. Rev. 111, 831-870 (2021). doi:10.1257/aer.20191777 · DOI · Paper page
- C. A. Bail et al., Proc. Natl. Acad. Sci. U.S.A. 115, 9216-9221 (2018).
- M. Prior, Post-Broadcast Democracy: How Media Choice Increases Inequality in Political Involvement and Polarizes Elections (Cambridge University Press, 2007).
- A. Acerbi, S. Altay, H. Mercier, Research note: Fighting misinformation or fighting for information? Harvard Kennedy School (HKS) Misinformation Review (2022); https://doi.org/ 10.37016/mr-2020-87. · DOI · Paper page
- A. Guess, K. Aslett, J. Tucker, R. Bonneau, J. Nagler, J. Quant. Descr. Digit. Media 10.51685/jqd.2021.006 (2021). · DOI
- P. A. Dow, L. Adamic, A. Friggeri, in Proceedings of the International AAAI Conference on Web and Social Media (2013), vol. 7, pp. 145-154.
Cited by
Sorted by citations
Misunderstanding the harms of online misinformation
Budak, C., Nyhan, B., Rothschild, D. M., Thorson, E., & Watts, D. J. (2024) · Nature · doi:10.1038/s41586-024-07417-w · Cited by 156 · see citing works
Self-citationNegative online news articles are shared more to social media
Watson, J., van der Linden, S., Watson, M., & Stillwell, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-71263-z · Cited by 70 · see citing works
How to study democratic backsliding
Druckman, J. N. (2024) · Political Psychology · doi:10.1111/pops.12942 · Cited by 56 · see citing works
Milli, S., Carroll, M., Wang, Y., Pandey, S., & Dragan, A. D. (2023) · arXiv.org · doi:10.48550/arxiv.2305.16941 · Cited by 200 · see citing works
On manipulation by emotional AI: UK adults’ views and governance implications
Bakir, V., Laffer, A., McStay, A., Miranda, D., & Urquhart, L. (2024) · Frontiers in Sociology · doi:10.3389/fsoc.2024.1339834 · Cited by 34 · see citing works
Ranking for engagement: How social media algorithms fuel misinformation and polarization
Germano, F., Gómez, V., & Sobbrio, F. (2026) · Journal of Public Economics · doi:10.1016/j.jpubeco.2026.105589 · Cited by 14 · see citing works
- Authors
- Guess, A. M., Malhotra, N., Pan, J., Barberá, P., Allcott, H., Brown, T., Crespo-Tenorio, A., Dimmery, D., Freelon, D., Gentzkow, M., González-Bailón, S., Kennedy, E., Kim, Y. M., Lazer, D., Moehler, D., Nyhan, B., Rivera, C. V., Settle, J., Thomas, D. R., Thorson, E., Tromble, R., Wilkins, A., Wojcieszak, M., Xiong, B., de Jonge, C. K., Franco, A., Mason, W., Stroud, N. J., & Tucker, J. A.
- Affiliations
- Princeton University (US)
- Stanford University (US)
- Meta (United States) (US)
- University of Vienna (AT)
- University of North Carolina at Chapel Hill (US)
- Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, USA.
- Carnegie Mellon University (US)
- University of Wisconsin–Madison (US)
- Northeastern University (US)
- Dartmouth College (US)
- William & Mary (US)
- Syracuse University (US)
- George Washington University (US)
- University of California, Davis (US)
- Meta, Menlo Park, CA, USA, CA, USA.
- Center for Media Engagement (US)
- New York University (US)
- Corresponding author
- Guess, Andrew M.
- ORCID
- Guess, Andrew M.
- Malhotra, Neil
- Pan, Jennifer
- Barberá, Pablo
- Brown, Taylor
- Dimmery, Drew
- Freelon, Deen
- Gentzkow, Matthew
- González-Bailón, Sandra
- Kennedy, Edward
- Kim, Young Mie
- Lazer, David
- Moehler, Devra
- Nyhan, Brendan
- Settle, Jaime
- Thomas, Daniel Robert
- Thorson, Emily
- Tromble, Rebekah
- Wilkins, Arjun
- Wojcieszak, Magdalena
- Mason, Winter
- Stroud, Natalie Jomini
- Tucker, Joshua A.
- Venue
- Science
- Published
- 2023-07-28
- Volume
- 381
- Issue
- 6656
- Pages
- 404-408
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W4385298633
- Cited by
- 184
- References
- 13
- Citations by year
- 2026: 62 · 2025: 52 · 2024: 53 · 2023: 12 · 2022: 3 · 2021: 2
- FWCI
- 76.92
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- UNC Libraries
- Funders
- Alfred P. Sloan Foundation
- American Association for the Advancement of Science
- William and Flora Hewlett Foundation
- Nutrition Obesity Research Center, University of North Carolina
- National Opinion Research Center
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Social Sciences › Sociology and Political Science › Media Influence and Politics
- Keywords
- Facebook, 2020 US presidential election, political polarization, political attitudes
- MeSH terms
- Attitude, Humans, Politics, Knowledge, Uncertainty, Social Media
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