Fighting Fake News with Peer Feedback: Theory and Experiment

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.