网络错误信息模型

Review of Economic Studies · 2023 · [{"name": "Daron Acemoğlu", "affiliation": ["Center for Economic and Policy Research", "Massachusetts Institute of Technology"]}, {"name": "Asuman Ozdaglar", "affiliation": ["Massachusetts Institute of Technology"]}, {"name": "James Siderius", "affiliation": ["Dartmouth College"]}]

中文摘要

本文提出一个网络内容分享模型：行动者依次观察一篇文章，并决定是否将其分享给他人；该内容可能包含也可能不包含错误信息。每个行动者最初持有一种意识形态偏向，会从社交媒体上的正面互动中获得效用，但同时不希望因传播错误信息而被公开指责。我们刻画了这一社交媒体博弈的（贝叶斯－纳什）均衡，并证明该博弈具有策略互补性。在此框架下，我们研究了以最大化用户参与度为目标的平台将如何设计其算法。主要结果表明：当相关文章可靠性较低、因而很可能包含错误信息时，最大化参与度的算法会采取“过滤气泡”的形式——即在观点相近的用户之间形成回音室。此外，社会两极分化程度越高、内容越具分裂性，过滤气泡就越可能出现。最后，我们讨论了针对这类由平台制造的错误信息的若干监管方案。

Abstract

Abstract We present a model of online content sharing where agents sequentially observe an article and decide whether to share it with others. This content may or may not contain misinformation. Each agent starts with an ideological bias and gains utility from positive social media interactions but does not want to be called out for propagating misinformation. We characterize the (Bayesian–Nash) equilibria of this social media game and establish that it exhibits strategic complementarities. Under this framework, we study how a platform interested in maximizing engagement would design its algorithm. Our main result establishes that when the relevant articles have low-reliability and are thus likely to contain misinformation, the engagement-maximizing algorithm takes the form of a “filter bubble”—creating an echo chamber of like-minded users. Moreover, filter bubbles become more likely when there is greater polarization in society and content is more divisive. Finally, we discuss various regulatory solutions to such platform-manufactured misinformation.