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Cited by 1,715Misinformation and Its ImpactsCommunication and COVID-19 ImpactAcademic Publishing and Open Access

The DOI matches its Crossref record; 38 of the 41 references it cites were found in a registry or an index, and 3 are not checked yet.

Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention

Open access
Pennycook, Gordon, McPhetres, Jonathon, Zhang, Yunhao, Lu, Jackson G., Rand, David G.·2020·Psychological Science
DOI 10.1177/0956797620939054PMC PMC7366427
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Abstract

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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Crossref · 10.1177/0956797620939054

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38 of 41 references verified0 citation errors · 0 not checkable

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Fig. 1. Results from Study 1: percentage of “yes” responses for each combination of headline veracity (true vs. false) and condition (accuracy = “To the best of your knowledge, is the claim in the above headline accurate?” vs. sharing = “Would you consider sharing this story online (for example, through Facebook or Twitter)?”). Error bars indicate 95% confidence intervals.
Fig. 1. Results from Study 1: percentage of “yes” responses for each combination of headline veracity (true vs. false) and condition (accuracy = “To the best of your knowledge, is the claim in the above headline accurate?” vs. sharing = “Would you consider sharing this story online (for example, through Facebook or Twitter)?”). Error bars indicate 95% confidence intervals.CC BY · PubMed Central
Fig. 2. Results from Study 2: percentage of headlines participants said they would be likely to share, separately for each combination of headline veracity (true vs. false) and condition (control vs. treatment). For this visualization, we discretize sharing intentions using the scale midpoint (i.e., 1–3 = 0, 4–6 = 1) to give a more easily interpretable measurement; all analyses are conducted using the full (nondiscretized) scale, and plotting the average (nondiscretized) sharing intentions looks qualitatively similar. For the equivalent plot using mean sharing intentions instead of the discretized percentages, see Figure S1 in the Supplemental Material available online. Error bars indicate 95% confidence intervals.
Fig. 2. Results from Study 2: percentage of headlines participants said they would be likely to share, separately for each combination of headline veracity (true vs. false) and condition (control vs. treatment). For this visualization, we discretize sharing intentions using the scale midpoint (i.e., 1–3 = 0, 4–6 = 1) to give a more easily interpretable measurement; all analyses are conducted using the full (nondiscretized) scale, and plotting the average (nondiscretized) sharing intentions looks qualitatively similar. For the equivalent plot using mean sharing intentions instead of the discretized percentages, see Figure S1 in the Supplemental Material available online. Error bars indicate 95% confidence intervals.CC BY · PubMed Central
Fig. 3. Relationship between the effect of the treatment in Study 2 and the average accuracy rating from participants in the accuracy condition of Study 1 as a function of headline veracity (true vs. false). The dashed line shows the best-fitting regression.
Fig. 3. Relationship between the effect of the treatment in Study 2 and the average accuracy rating from participants in the accuracy condition of Study 1 as a function of headline veracity (true vs. false). The dashed line shows the best-fitting regression.CC BY · PubMed Central

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Research works printed but not linked: 2

  1. Adhanom Ghebreyesus T. (2020, February 15). Munich Security Conference. World Health Organization. Retrieved from https://www.who.int/dg/speeches/detail/munich-security-conference
  2. Ross R. M., 2019, PsyArXiv

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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
ORCID
Venue
Psychological Science
Publisher
SAGE Publications
Published
2020-06-30
Volume
31
Issue
7
Pages
770-780
ISSN
  • 0956-7976 (ISSN-L)
  • 1467-9280
DOI
10.1177/0956797620939054
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
FWCI
393.97
Type
journal article
Language
en
Open access
Licence
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 › Library and Information Sciences › 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

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