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Community-level bans are a common tool against groups that enable onlineharassment and harmful speech. Unfortunately, the efficacy of community banshas only been partially studied and with mixed results. Here, we provide aflexible unsupervised methodology to identify in-group language and track useractivity on Reddit both before and after the ban of a community (subreddit). Weuse a simple word frequency divergence to identify uncommon wordsoverrepresented in a given community, not as a proxy for harmful speech but asa linguistic signature of the community. We apply our method to 15 bannedsubreddits, and find that community response is heterogeneous betweensubreddits and between users of a subreddit. Top users were more likely tobecome less active overall, while random users often reduced use of in-grouplanguage without decreasing activity. Finally, we find some evidence that theeffectiveness of bans aligns with the content of a community. Users of darkhumor communities were largely unaffected by bans while users of communitiesorganized around white supremacy and fascism were the most affected.Altogether, our results show that bans do not affect all groups or usersequally, and pave the way to understanding the effect of bans acrosscommunities.

When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban