Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention
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Across two studies with more than 1,700 U.S. adults recruited online, we present evidence that people share false claims about COVID-19 partly because they simply fail to think sufficiently about whether or not the content is accurate when deciding what to share. In Study 1, participants were far worse at discerning between true and false content when deciding what they would share on social media relative to when they were asked directly about accuracy. Furthermore, greater cognitive reflection and science knowledge were associated with stronger discernment. In Study 2, we found that a simple accuracy reminder at the beginning of the study (i.e., judging the accuracy of a non-COVID-19-related headline) nearly tripled the level of truth discernment in participants’ subsequent sharing intentions. Our results, which mirror those found previously for political fake news, suggest that nudging people to think about accuracy is a simple way to improve choices about what to share on social media.
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Under current plausible COVID-19 vaccine acceptance rates, possible levels of existing protective immunity, and the rapidly evolving nature of misinformation, it is unclear whether vaccination will reach the levels required for herd immunity.
“Under these current plausible COVID-19 vaccine acceptance rates, possible levels of existing protective immunity-though it is unclear whether post-infection immunity confers long-term immunity 29 and the rapidly evolving nature of misinformation surrounding the pandemic 23,30 , it is unclear whether vaccination will reach the levels required for herd immunity.”
From Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA (2021) · cites this paper as [23]
The citation is attached to the statement about pandemic misinformation, not the citing authors’ conclusion about herd immunity. The paper’s Abstract says COVID-19 misinformation has proliferated and takes many forms, but it does not establish that the misinformation is rapidly evolving. (LLM, conf=0.78)
Experimental — accuracy not yet measuredExperimental work indicates that directing users' attention to accuracy can improve sharing quality and reduce misinformation circulation.
“Experimental work indicates that directing users' attention to accuracy can improve sharing quality and reduce misinformation circulation (Pennycook et al., 2020(Pennycook et al., , 2021;;Roozenbeek et al., 2021).”
From Sensational Headlines, Engagement, and Credibility in Nepal’s Online News (2025) · cites this paper as Pennycook et al., 2020
Accuracy prompts can modestly improve users' sharing decisions.
“Pennycook et al. (2020) and Roozenbeek et al. (2021) show that accuracy prompts can modestly improve users' sharing decisions, while Pennycook et al. (2021) argue that shifting attention toward accuracy can reduce misinformation sharing online.”
From Sensational Headlines, Engagement, and Credibility in Nepal’s Online News (2025) · cites this paper as Pennycook et al. (2020)
A growing number of empirical studies have uncovered evidence that people update their political attitudes and beliefs in line with evidence and arguments presented to them.
“Consistent with this view, a growing number of empirical studies have uncovered evidence that people update their political attitudes and beliefs in line with evidence (e.g., refs. 4,13,27–29) and arguments23,30,31 presented to them.”
From Factual knowledge can reduce attitude polarization (2025) · cites this paper as [28]
Study 1 Results compare judgments of COVID-19 headline accuracy with willingness to share; Study 2 Results report that an accuracy reminder changed sharing intentions. Neither section reports people updating political attitudes or beliefs in response to evidence or arguments. Our copy provides no page numbers for these passages. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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What types of COVID-19 conspiracies are populated by Twitter bots?
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“The work by Pennycook and colleagues epitomizes the seriousness of this problem by showing, with a social experiment including 1,600 participants, that subjects tend to share misinformation and false claims about COVID-19 predominantly because they are unable to determine whether the content is scientifically sound an…”
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Identifying Fake News on Social Networks Based on Natural Language Processing: Trends and Challenges
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(Why) Is Misinformation a Problem?
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How to think about whether misinformation interventions work
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News from Generative Artificial Intelligence Is Believed Less
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Beyond “fake news”: Analytic thinking and the detection of false and hyperpartisan news headlines
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The COVID-19 Misinfodemic: Moving Beyond Fact-Checking
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COVID-19-Related Social Media Fake News in India
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Online searches to evaluate misinformation can increase its perceived veracity
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Belief in Science Influences Physical Distancing in Response to COVID-19 Lockdown PoliciesPreprint
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- Authors
- Pennycook, G., McPhetres, J., Zhang, Y., Lu, J. G., & Rand, D. G.
- Affiliations
- University of Regina (CA)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Pennycook, Gordon
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2020-06-30
- Volume
- 31
- Issue
- 7
- Pages
- 770-780
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- PMC
- PMC7366427
- OpenAlex
- W3013473577
- Cited by
- 1,715
- References
- 38
- Citations by year
- 2026: 111 · 2025: 188 · 2024: 238 · 2023: 299 · 2022: 329 · 2021: 416 · 2020: 132 · 2019: 2
- FWCI
- 393.97
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Psychological Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- miami foundation
- William and Flora Hewlett Foundation
- Omidyar Network
- John Templeton Foundation
- Canadian Institutes of Health Research
- Social Sciences and Humanities Research Council of Canada
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Communication and COVID-19 Impact
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- COVID-19 misinformation, social media sharing, truth discernment, fake news, cognitive reflection, scientific knowledge, misinformation prevention, sharing intention
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Adolescent, Adult, Aged, Aged, 80 and over, Communication, Decision Making, Female, Humans, Male, Middle Aged, Pneumonia, Viral, Coronavirus Infections, Information Dissemination, Young Adult, Pandemics, 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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