理性沉默与虚假极化：观点组织与推荐系统如何扭曲公众舆论的表达
Rational Silence and False Polarization: How Viewpoint Organizations and Recommender Systems Distort the Expression of Public Opinion
AI总结：
针对社交媒体平台对在线话语观察者认知社区观点的影响被忽视的问题，本文构建嵌套博弈论模型分析用户、观点组织与推荐系统的互动，揭示其导致温和用户理性沉默与虚假极化的机制，并提出平台改进策略。
摘要：
基于人工智能的社交媒体平台已改变经济与社会互动的本质。人工智能实现了我们如今习以为常的大规模、高度个性化的在线信息共享。社交媒体平台似乎助长的极化现象已受到广泛关注。然而，这些人工智能驱动的平台带来的转型中，一个关键影响却较少被关注：平台如何影响在线话语观察者对社区观点的认知。这些观察者包括政策制定者和立法者（他们通过社交媒体评估政策和立法变革的前景），以及基于大规模互联网数据训练人工智能模型的开发者（其输出可能同样反映对公众舆论的扭曲认知）。本文提出嵌套博弈论模型，展示观察到的在线舆论如何由以下三方决策互动产生：用户关于是否及以何种修辞强度在平台分享观点的决策、试图鼓励或阻止在线观点分享的组织（如传统媒体和倡导组织）的努力、社交媒体平台控制的人工智能推荐系统的运作。研究表明，意识形态组织的信号会促使修辞强度增加，导致温和用户“理性沉默（不执行）”，进而造成对平均观点的极化印象。研究还表明，鼓励在线社区形成的推荐系统（这些社区最终看到的是观点的偏斜样本）也会放大这种观察到的极化。此外，研究确定了平台可实施的实用策略，例如减少对意识形态组织信号的接触，以及采用量身定制的内容 moderation 方法。
英文摘要：
AI-based social media platforms has already transformed the nature of economic and social interaction. AI enables the massive scale and highly personalized nature of online information sharing that we now take for granted. Extensive attention has been devoted to the polarization that social media platforms appear to facilitate. However, a key implication of the transformation we are experiencing due to these AI-powered platforms has received much less attention: how platforms impact what observers of online discourse come to believe about community views. These observers include policymakers and legislators, who look to social media to gauge the prospects for policy and legislative change, as well as developers of AI models trained on large-scale internet data, whose outputs may similarly reflect a distorted view of public opinion. In this paper, we present a nested game-theoretic model to show how observed online opinion is produced by the interaction of the decisions made by users about whether and with what rhetorical intensity to share their opinions on a platform, the efforts of organizations (such as traditional media and advocacy organizations) that seek to encourage or discourage opinion-sharing online, and the operation of AI-powered recommender systems controlled by social media platforms. We show that signals from ideological organizations encourage an increase in rhetorical intensity, leading to the 'rational silence' of moderate users. This, in turn, creates a polarized impression of where average opinions lie. We also show that this observed polarization can also be amplified by recommender systems that encourage the formation of communities online that end up seeing a skewed sample of opinion. We also identify practical strategies platforms can implement, such as reducing exposure to signals from ideological organizations and a tailored approach to content moderation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
