  Shadow banning consists for an online social network 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 or not 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
million 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-interactions 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.
