The Misleading count: an identity-based intervention to counter partisan misinformation sharing
Open accessAbstract
Interventions to counter misinformation are often less effective for polarizing content on social media platforms. We sought to overcome this limitation by testing an identity-based intervention, which aims to promote accuracy by incorporating normative cues directly into the social media user interface. Across three pre-registered experiments in the US ( N = 1709) and UK ( N = 804), we found that crowdsourcing accuracy judgements by adding a Misleading count (next to the Like count) reduced participants' reported likelihood to share inaccurate information about partisan issues by 25% (compared with a control condition). The Misleading count was also more effective when it reflected in-group norms (from fellow Democrats/Republicans) compared with the norms of general users, though this effect was absent in a less politically polarized context (UK). Moreover, the normative intervention was roughly five times as effective as another popular misinformation intervention (i.e. the accuracy nudge reduced sharing misinformation by 5%). Extreme partisanship did not undermine the effectiveness of the intervention. Our results suggest that identity-based interventions based on the science of social norms can be more effective than identity-neutral alternatives to counter partisan misinformation in politically polarized contexts (e.g. the US). This article is part of the theme issue ‘Social norm change: drivers and consequences’.
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These interventions have typically modest effects compared to alternative strategies.
“However, these interventions have typically modest effects compared to alternative strategies (36).”
From The impact of generative artificial intelligence on socioeconomic inequalities and policy making (2024) · cites this paper as [36]
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The role of political devotion in sharing partisan misinformation and resistance to fact-checkingPreprint
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The impact of generative artificial intelligence on socioeconomic inequalities and policy making
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- Authors
- Pretus, C., Javeed, A. M., Hughes, D., Hackenburg, K., Tsakiris, M., Vilarroya, O., & Van Bavel, J. J.
- Affiliations
- Universitat Autònoma de Barcelona (ES)
- New York University (US)
- University of London (GB)
- Centre for the Politics of Feelings, School of Advanced Study, Royal Holloway, University of London, London WC1E 7HU, UK
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
- Department of Psychology and Center for Neural Science, New York University, New York, NY 10003, USA
- Corresponding author
- Pretus, Clara
- Venue
- Philosophical Transactions of the Royal Society B Biological Sciences
- Publisher
- The Royal Society
- Published
- 2024-01-22
- Volume
- 379
- Issue
- 1897
- Pages
- 20230040-20230040
- ISSN
- 0962-8436 (ISSN-L)
- 1471-2970
- PMC
- PMC10799730
- OpenAlex
- W4391058501
- Cited by
- 35
- References
- 39
- Citations by year
- 2026: 7 · 2025: 7 · 2024: 13 · 2023: 7 · 2022: 1
- FWCI
- 19.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Philosophical Transactions of the Royal Society B Biological Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Dipòsit Digital de Documents de la UAB (Universitat Autònoma de Barcelona) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- Funders
- NOMIS Stiftung
- HORIZON EUROPE European Innovation Council (FETPROACT-EIC-05-2019)
- 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
- misinformation sharing, social norms, political polarization
- MeSH terms
- Communication, Cues, Humans, Judgment, Probability, Social Norms
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