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