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

TL;DR

This study introduces an unsupervised method to analyze how community-specific language changes after subreddit bans, revealing heterogeneous effects across different communities and user types.

Contribution

The paper presents a novel linguistic divergence approach to assess community and user responses to bans, highlighting varied impacts based on community content.

Findings

Top users tend to reduce activity after bans.

Random users often stop using in-group language without reducing activity.

Effectiveness of bans varies with community content, affecting some groups more than others.

Abstract

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.