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

Community-level bans are a common tool against groups that enable online\nharassment and harmful speech. Unfortunately, the efficacy of community bans\nhas only been partially studied and with mixed results. Here, we provide a\nflexible unsupervised methodology to identify in-group language and track user\nactivity on Reddit both before and after the ban of a community (subreddit). We\nuse a simple word frequency divergence to identify uncommon words\noverrepresented in a given community, not as a proxy for harmful speech but as\na linguistic signature of the community. We apply our method to 15 banned\nsubreddits, and find that community response is heterogeneous between\nsubreddits and between users of a subreddit. Top users were more likely to\nbecome less active overall, while random users often reduced use of in-group\nlanguage without decreasing activity. Finally, we find some evidence that the\neffectiveness of bans aligns with the content of a community. Users of dark\nhumor communities were largely unaffected by bans while users of communities\norganized around white supremacy and fascism were the most affected.\nAltogether, our results show that bans do not affect all groups or users\nequally, and pave the way to understanding the effect of bans across\ncommunities.\n