In a pre-registered randomized experiment, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm amplifies emotionally charged, out-group hostile content that users say makes them feel worse about their political out-group. Furthermore, we find that users do not prefer the political tweets selected by the algorithm, suggesting that the engagement-based algorithm underperforms in satisfying users' stated preferences. Finally, we explore the implications of an alternative approach that ranks 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，与基于逆时间顺序的基线相比，推特基于参与度的排序算法放大了情绪化、针对外群体的敌对内容，而用户表示这些内容让他们对外群体产生更负面的感受。此外，我们发现用户并不偏好该算法选出的政治推文，表明基于参与度的算法在满足用户明确表达的偏好方面表现欠佳。最后，我们探索了一种基于用户明确偏好进行内容排序的替代方法，发现该方法减少了愤怒、党派偏见和外群体敌对内容，但可能加剧了回音室效应。这些证据强调了需要一种更细致的内容排序方法，以平衡参与度、用户明确偏好及社会政治结果。