为参与度排名：社交媒体算法如何助长错误信息和极化
Ranking for engagement: How social media algorithms fuel misinformation and polarization
Journal of Public Economics · 2026 · [{"name": "Fabrizio Germano", "affiliation": ["Universitat Pompeu Fabra", "Barcelona School of Economics"]}, {"name": "Vicenç Gómez", "affiliation": ["Universitat Pompeu Fabra"]}, {"name": "Francesco Sobbrio", "affiliation": ["University of Rome Tor Vergata"]}]
中文摘要
社交媒体是围绕极化、错误信息乃至世界各地民主状况的无数争论的中心。社交媒体的一个基本特征是其推荐算法，该算法决定了呈现给用户的内容排序。本文研究推荐算法与用户行为之间的动态反馈循环，并构建一个理论框架，以评估基于流行度的参数对平台参与度、错误信息和极化的影响。该模型揭示了一个根本性权衡：对点赞和分享等在线社交互动赋予更高权重，会提高用户参与度，但也会增加错误信息（挤出真相）和极化。基于这一洞见，分析考察了对社交互动征收简单的“参与税”，如何通过改变平台在设计利润最大化算法时的激励，来缓解这些负外部性。该框架被扩展至包含个性化排序，结果表明个性化会进一步放大极化。最后，来自意大利和美国调查数据的实证证据表明，Facebook 2018年的“有意义的社交互动”更新——增加了对某些参与度指标的强调——助长了意识形态极端主义和情感极化的加剧。
Abstract
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.