Algorithms play a critical role in steering online attention on social media. Many have alleged that algorithms can perpetuate bias. This study audited shadowbanning, where a user or their content is temporarily hidden on Twitter. We repeatedly tested whether a stratified random sample of American Twitter accounts (n ≈ 25,000) had been subject to various forms of shadowbans. We then identified the type of user and tweet characteristics that predict a shadowban. In general, shadowbans are rare. We found that accounts with bot-like behavior were more likely to face shadowbans, while verified accounts were less likely to be shadowbanned. The replies by Twitter accounts that posted offensive tweets and tweets about politics (from both the left and the right) were more likely to be downtiered. The findings have implications for algorithmic accountability and the design of future audit studies of social media platforms.

中文翻译：

---

在社交媒体上保持沉默：美国推特世界中影子禁令的把关功能

算法在引导社交媒体上的在线注意力方面发挥着关键作用。许多人声称算法可以使偏见永久化。这项研究审核了 shadowbanning，即用户或他们的内容暂时隐藏在 Twitter 上。我们反复测试了美国 Twitter 帐户的分层随机样本 (n ≈ 25,000) 是否受到各种形式的影子禁令。然后，我们确定了预测影子禁令的用户类型和推文特征。一般来说，shadowbans 很少见。我们发现具有类似机器人行为的帐户更有可能面临影子禁令，而经过验证的帐户则不太可能被影子禁令。发布攻击性推文和有关政治的推文（来自左翼和右翼）的 Twitter 帐户的回复更有可能被降级。