遏制虚假新闻，放大真相

摘要

We develop a comprehensive framework to evaluate policy interventions aimed at curbing false news dissemination on social media. Using a randomized experiment on Twitter and X during the 2022 and 2024 U.S. elections, we assess priming for misinformation awareness, fact‐checking, confirmation clicks, and content consideration prompts. Priming proves most effective in reducing false‐news sharing while preserving true news dissemination. We build and structurally estimate a model of sharing, motivated by partisan persuasion, partisan signaling, and reputational concerns. We identify three channels through which policies influence sharing: (i) updating perceived veracity and partisanship of content, (ii) raising salience of reputation, and (iii) increasing engagement costs. Differences in the effects of policies are explained by the salience and cost channels. Content‐neutral priming is best at enhancing salience of reputation at minimal cost and almost as effective as fact‐checking in updating veracity. Salience channel is stronger when users encounter uncontroversially true content.