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Setting the Record Straighter on Shadow Banning

机译：在阴影禁止上设置唱片

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Shadow banning consists for an online social net-work in limiting the visibility of some of its users, without them being aware of it. Twitter declares that it does not use such a practice, sometimes arguing about the occurrence of "bugs" to justify restrictions on some users. This paper is the first to address the plausibility of shadow banning on a major online platform, by adopting both a statistical and a graph topological approach.We first conduct an extensive data collection and analysis campaign, gathering occurrences of visibility limitations on user profiles (we crawl more than 2.5 millions of them). In such a black-box observation setup, we highlight the salient user profile features that may explain a banning practice (using machine learning predictors). We then pose two hypotheses for the phenomenon: i) limitations are bugs, as claimed by Twitter, and ii) shadow banning propagates as an epidemic on user-interaction ego-graphs. We show that hypothesis i) is statistically unlikely with regards to the data we collected. We then show some interesting correlation with hypothesis ii), suggesting that the interaction topology is a good indicator of the presence of groups of shadow banned users on the service.
机译：阴影禁止包括在线社交网络，以限制其中一些用户的可见性，而无需他们意识到这一点。 Twitter声明它不使用这样的练习，有时争论发生“错误”的发生，以证明对某些用户的限制。本文首先通过采用统计和图形拓扑方法来解决主要在线平台上的阴影禁止禁止的合理性。我们首先进行广泛的数据收集和分析活动，收集用户配置文件的可见性限制的发生（我们爬行超过2.5百万升）。在这种黑匣子观测设置中，我们突出显示了可以解释禁止练习的突出用户配置文件（使用机器学习预测器）。然后，我们为这一现象构成了两个假设：i）局限性是漏洞，如Twitter所声称，II）影子禁止作为用户交互自我图的疫情传播。我们表明，关于我们收集的数据，假设I）在统计上不太可能。然后，我们与假设II展示了一些有趣的相关性，这表明互动拓扑是在服务上禁止用户组的良好指标。

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