Ranking for engagement: How social media algorithms fuel misinformation and polarization

Social media are at the center of countless debates on polarization, misinformation, and even the state of democracy in various parts of the world. An essential feature of social media is their recommendation algorithm that determines the ranking of content presented to the users. This paper investigates the dynamic feedback loop between recommendation algorithms and user behavior, and develops a theoretical framework to assess the impact of popularity-based parameters on platform engagement, misinformation, and polarization. The model uncovers a fundamental trade-off: assigning greater weight to online social interactions—such as likes and shares—increases user engagement but also increases misinformation ( crowding-out the truth ) and polarization. Building on this insight, the analysis considers how a simple “engagement tax” on social interactions can mitigate these negative externalities by altering platform incentives in the design of profit-maximizing algorithms. The framework is extended to include personalized rankings, demonstrating that personalization further amplifies polarization. Finally, empirical evidence from survey data in Italy and the United States indicates that Facebook’s 2018 “Meaningful Social Interactions” update—which increased the emphasis on certain engagement metrics—contributed to increased ideological extremism and affective polarization.