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.

Keywords

HeadlinePsychologyMisinformationSocial mediaDiscernmentSocial psychologyCoronavirus disease 2019 (COVID-19)Social distanceFake newsConfirmation biasMasking (illustration)Internet privacyAdvertisingComputer scienceEpistemology

MeSH Terms

AdolescentAdultAgedAged80 and overBetacoronavirusCOVID-19CommunicationCoronavirus InfectionsDecision MakingFemaleHumansInformation DisseminationMaleMiddle AgedPandemicsPneumoniaViralSARS-CoV-2Social MediaYoung Adult

Affiliated Institutions

Related Publications

Publication Info

Year
2020
Type
article
Volume
31
Issue
7
Pages
770-780
Citations
1661
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1661
OpenAlex
80
Influential
1149
CrossRef

Cite This

Gordon Pennycook, Jonathon McPhetres, Yunhao Zhang et al. (2020). Fighting COVID-19 Misinformation on Social Media: Experimental Evidence for a Scalable Accuracy-Nudge Intervention. Psychological Science , 31 (7) , 770-780. https://doi.org/10.1177/0956797620939054

Identifiers

DOI
10.1177/0956797620939054
PMID
32603243
PMCID
PMC7366427

Data Quality

Data completeness: 90%