POLITICAL ECONOMY: Curtailing False News, Amplifying Truth; Professor Emeric Henry (Sciences Po)

Abstract

We develop a comprehensive framework to assess policy measures aimed at curbing false news dissemination on social media. Using a randomized experiment on Twitter during the 2022 U.S. mid-term elections, we evaluate such policies as priming misinformation awareness, fact-checking, confirmation clicks, and content consideration prompts. Priming is the most effective in reducing sharing of false while increasing sharing of true news. We build and structurally estimate a model of sharing decisions, motivated by persuasion, partisan signaling, and reputation. The model identifies three channels through which policies affect sharing: (i) updating perceived content veracity and partisanship, (ii) raising the salience of reputation, and (iii) increasing sharing frictions. We find that all policies impact sharing via the salience of reputation and cost of friction. Affecting perceived veracity plays a negligible role as a mechanism in all policies, including fact-checking. The priming intervention performs best in enhancing reputation salience with minimal added friction.