In a pre-registered randomized experiment, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm may amplify emotionally charged, out-group hostile content and contribute to affective polarization. Furthermore, we critically examine the claim that the algorithm shows users what they want to see, discovering that users do not prefer the political tweets selected by the algorithm. Finally, we explore the implications of an alternative approach to ranking content based on users' stated preferences and find a reduction in angry, partisan, and out-group hostile content but also a potential reinforcement of echo chambers. The evidence underscores the necessity for a more nuanced approach to content ranking that balances engagement, users' stated preferences, and sociopolitical outcomes.

翻译：在一项预先注册的随机实验中，我们发推特现，与按时间逆序排列的基线相比，推特基于参与度的排名算法可能放大了情绪化、针对外群体的敌对内容，并加剧了情感极化。此外，我们批判性地审视了“该算法向用户展示他们想看的内容”这一主张，发现用户并不偏好算法所选出的政治推文。最后，我们探索了一种基于用户明确偏好进行内容排名的替代方法，发现这虽然减少了愤怒、党派性和针对外群体的敌对内容，但可能强化了信息茧房。证据表明，有必要采用一种更为细致的内容排名方法，以平衡参与度、用户明确偏好与社会政治结果。