Fighting Fake News with Peer Feedback: Theory and Experiment

Yasushi Asako (), Yoshio Kamijo (), Daiki Kishishita () and Masayuki Odora ()
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Yasushi Asako: Faculty of Political Science and Economics, Waseda University
Yoshio Kamijo: Faculty of Political Science and Economics, Waseda University
Daiki Kishishita: Graduate School of Economics, Hitotsubashi University
Masayuki Odora: Global Education Center, Waseda University

No 2531, Working Papers from Waseda University, Faculty of Political Science and Economics

Abstract: The diffusion of fake news on social media poses a growing challenge to society. This study develops a theoretical model and laboratory experiment to examine whether user-to-user feedback such as likes or negative comments can reduce fake news sharing. We construct a sender-receiver game in which the sender receives a signal from potentially unreliable sources and decides whether to share it, while feedback from the receiver enables learning about information quality over time. The model predicts that (i) peer feedback induces self-selection: individuals with unreliable sources learn to stop sharing, and (ii) fake news spreads more when senders are motivated by reputation rather than accuracy, though this effect is modest. We test these predictions by developing a novel lab experiment based on the ball-and-urn experimental design. Consistent with the theory, feedback reduces fake news sharing, but effects are weaker due to underreaction in belief updating and noisy decisions. Differences across motivational incentives are minimal. These findings highlight both the potential and limits of peer feedback in preventing fake news sharing, offering implications for platforms seeking to curb misinformation through user-to-user feefback.

Keywords: Fake news; Communication; Social learning; Peer feedback; Social media (search for similar items in EconPapers)
JEL-codes: C92 D72 D83 L82 (search for similar items in EconPapers)
Pages: 55 pages
Date: 2026-02
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