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，我们发现，相对于纯时间倒序基线，Twitter基于参与度的排序算法可能会放大情绪化、针对外群体的敌对性内容，并助长情感极化。此外，我们对“算法向用户展示他们想看的内容”这一说法进行了批判性检验，发现用户实际上*并不*偏好算法挑选的政治推文。最后，我们探讨了基于用户明确偏好进行内容排序的替代方案的含义，发现这能减少愤怒性、党派性和针对外群体的敌对性内容，但也可能加剧回音室效应。这些证据表明，有必要采取一种更细致的内容排序方法，以平衡参与度、用户明确偏好与社会政治结果。