CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/0956797620939054

Cites

38 of 41 references verified0 citation errors · 0 not checkable

Statistics

Figures

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 38 references

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

Cited by

Sorted by citations

Items 901–1,000 of 1,004

Related

See all

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

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 ›

Are you an author, or do you see a problem with this page? Request a review