As social media continues to have a significant influence on public opinion, understanding the impact of the machine learning algorithms that filter and curate content is crucial. However, existing studies have yielded inconsistent results, potentially due to limitations such as reliance on observational methods, use of simulated rather than real users, restriction to specific types of content, or internal access requirements that may create conflicts of interest. To overcome these issues, we conducted a pre-registered controlled experiment on Twitter's algorithm without internal access. The key to our design was to, for a large group of active Twitter users, simultaneously collect (a) the tweets the personalized algorithm shows, and (b) the tweets the user would have seen if they were just shown the latest tweets from people they follow; we then surveyed users about both sets of tweets in a random order. Our results indicate that the algorithm amplifies emotional content, and especially those tweets that express anger and out-group animosity. Furthermore, political tweets from the algorithm lead readers to perceive their political in-group more positively and their political out-group more negatively. Interestingly, while readers generally say they prefer tweets curated by the algorithm, they are less likely to prefer algorithm-selected political tweets. Overall, our study provides important insights into the impact of social media ranking algorithms, with implications for shaping public discourse and democratic engagement.

翻译：随着社交媒体持续对公众舆论产生重大影响，理解过滤和筛选内容的机器学习算法的影响至关重要。然而，现有研究结果不一致，其潜在原因包括依赖观察性方法、使用模拟用户而非真实用户、局限于特定内容类型，或需要内部访问权限从而可能产生利益冲突。为克服这些问题，我们在未获得内部访问权限的情况下，对Twitter算法进行了一项预注册的受控实验。我们设计的关键在于，针对一批活跃的Twitter用户，同时收集（a）个性化算法展示的推文，以及（b）若仅按时间顺序显示所关注用户的最新推文时用户本应看到的推文；随后我们以随机顺序就两组推文对用户进行问卷调查。结果表明，该算法放大了情感化内容，尤其是表达愤怒和对外群体敌意的推文。此外，通过算法推荐的政治推文导致读者对其政治内群体产生更积极的看法，而对其政治外群体产生更消极的看法。有趣的是，尽管读者普遍表示更偏好由算法筛选的推文，但他们不太偏好算法推荐的政治推文。总体而言，我们的研究为社交媒体排序算法的影响提供了重要见解，并对塑造公共讨论和民主参与具有启示意义。