在基于流行度的排名中奖励参与和个性化会加剧极端化和极化
Rewarding Engagement and Personalization in Popularity-Based Rankings Amplifies Extremism and Polarization
AI总结：
通过实证驱动的模型、模拟和人类实验，揭示排名算法在用户位置偏差、同质偏好、极端用户活跃和流行度反馈条件下，奖励主动参与和个性化排名会不可避免地推动极端化和极化新闻消费。
摘要：
尽管已有大量研究，在线平台如何塑造极端化和极化的机制仍不清楚。我们识别并测试了一个基于实证证据的机制，解释了排名算法如何放大这两种现象。该机制基于有充分文献支持的假设：(i) 用户存在位置偏差，倾向于选择排名较高的项目；(ii) 用户偏好志同道合的内容；(iii) 观点更极端的用户更可能积极参与；(iv) 排名算法基于流行度，将点击量更高的项目置于更高位置。在这些条件下，当平台额外奖励主动参与并实施个性化排名时，用户不可避免地走向更极端和极化的新闻消费。我们在一个动态模型中形式化这一机制，并通过模拟和涉及数百名人类参与者的交互实验进行评估，其中排名根据用户活动动态更新。
英文摘要：
Despite extensive research, the mechanisms through which online platforms shape extremism and polarization remain poorly understood. We identify and test a mechanism, grounded in empirical evidence, that explains how ranking algorithms can amplify both phenomena. This mechanism is based on well-documented assumptions: (i) users exhibit position bias and tend to prefer items displayed higher in the ranking, (ii) users prefer like-minded content, (iii) users with more extreme views are more likely to engage actively, and (iv) ranking algorithms are popularity-based, assigning higher positions to items that attract more clicks. Under these conditions, when platforms additionally reward active engagement and implement personalized rankings, users are inevitably driven toward more extremist and polarized news consumption. We formalize this mechanism in a dynamical model, which we evaluate by means of simulations and interactive experiments with hundreds of human participants, where the rankings are updated dynamically in response to user activity.
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。
