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Fighting COVID-19 misinformation on social media: Experimental evidence for a scalable accuracy nudge intervention
Open accessAbstract
Across two studies with over 1,600 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 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 are asked directly about accuracy. Furthermore, 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 – more than doubled the level of truth discernment in participants’ 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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In the citing paper
“Moreover, there is mixed research regarding whether corrections may actually increase belief in the original misinformation 121,124-126 or in other misleading claims that fail to get corrected 127 .”
From Using social and behavioural science to support COVID-19 pandemic response (2020) · cites this paper as [127]
Experimental — accuracy not yet measuredIn the citing paper
“Recent work has observed that there is a disconnect between perceptions of accuracy and sharing intentions: Even when participants are quite good at distinguishing between true and false headlines (if they are asked to judge accuracy), this ability to discern truth from falsehood often does not translate to sharing24–26.”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [26]
In the citing paper
“Evidence for the role of inattention comes from experiments in which prompting participants to think about the concept of accuracy – for example, by asking them to evaluate the accuracy of a random headline at the beginning of the study – reduces the disconnect between accuracy judgments and sharing intentions, and thereby increases the quality of news shared24–26.”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [26]
In the citing paper
“given the dissociation between accuracy judgments and sharing intentions observed in past work24–26”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [26]
In the citing paper
“and that the accuracy prompts are effective because they draw attention back to accuracy24,26”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [26]
In the citing paper
“In the experiments analyzed here, the true headlines were typically fairly ambiguous - see, for example, pre-test data24 and accuracy-only conditions26 in recent accuracy prompt studies”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [26]
In the citing paper
“where only roughly 60% of true headlines were rated as accurate”
From Accuracy prompts are a replicable and generalizable approach for reducing the spread of misinformation (2022) · cites this paper as [26]
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Metadata
- 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
- Publisher
- Center for Open Science
- Published
- 2020-03-17
- OpenAlex
- W4232317699
- Cited by
- 390
- References
- 37
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 52 · 2023: 43 · 2022: 67 · 2021: 113 · 2020: 102
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion)
- Funders
- John Templeton Foundation
- William and Flora Hewlett Foundation
- Miami Foundation
- 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 › Library and Information Sciences › Academic Publishing and Open Access
- Keywords
- COVID-19 misinformation, social media sharing, truth discernment, fake news, cognitive reflection, scientific knowledge
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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