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Fighting the spread of COVID-19 misinformation in Kyrgyzstan, India, and the United States: how replicable are accuracy nudge interventions?

Gavin, Lyndsay, McChesney, Jenna, Tong, Anson, Sherlock, Joseph, Foster, Lori and Tomsa, Sergiu (2022) Fighting the spread of COVID-19 misinformation in Kyrgyzstan, India, and the United States: how replicable are accuracy nudge interventions? Technology, Mind, and Behavior, 3 (3). ISSN 2689-0208

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Identification Number: 10.1037/tmb0000086

Abstract

The spread of misinformation has generated confusion and uncertainty about how to behave with respect to protective actions during the COVID-19 pandemic, such as social distancing and getting vaccinated. Pennycook et al. (2020) garnered significant press attention when they found that asking people to think about the accuracy of a single headline (i.e., accuracy nudge) improved their discernment in sharing true versus false information related to COVID-19. The present Open Science Framework preregistered experiment sought to replicate the work of Pennycook et al. (2020) and test the generalizability of their findings to three different countries: Kyrgyzstan, India, and the United States. The present study also explores whether findings extend to information related to COVID-19 vaccine acceptance, a timely and important topic at the time of data collection. The accuracy nudge’s effect did not replicate in the Kyrgyzstan sample (n = 1,049). Results were mixed in India (n = 703) and the United States (n = 829); the nudge decreased willingness to share some misinformation but it did not significantly increase willingness to share true information. We discuss potential explanations for these findings and practical implications for those working to combat the spread of misinformation online.

Item Type: Article
Additional Information: © 2022 The Author(s)
Divisions: Psychological and Behavioural Science
Subjects: B Philosophy. Psychology. Religion > BF Psychology
H Social Sciences
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 17 Sep 2024 10:57
Last Modified: 17 Sep 2024 16:03
URI: http://eprints.lse.ac.uk/id/eprint/125432

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