Sergei Guriev, Emeric Henry, Théo Marquis, Ekaterina Zhuravskaya
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引用次数: 0
Abstract
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 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.
期刊介绍:
Econometrica publishes original articles in all branches of economics - theoretical and empirical, abstract and applied, providing wide-ranging coverage across the subject area. It promotes studies that aim at the unification of the theoretical-quantitative and the empirical-quantitative approach to economic problems and that are penetrated by constructive and rigorous thinking. It explores a unique range of topics each year - from the frontier of theoretical developments in many new and important areas, to research on current and applied economic problems, to methodologically innovative, theoretical and applied studies in econometrics.
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