Allison M. Roth


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Distinguishing In-Groups and Onlookers by Language Use
Joshua Minot | Milo Trujillo | Samuel Rosenblatt | Guillermo De Anda-Jáuregui | Emily Moog | Allison M. Roth | Briane Paul Samson | Laurent Hébert-Dufresne
Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis

Inferring group membership of social media users is of high interest in many domains. Group membership is typically inferred via network interactions with other members, or by the usage of in-group language. However, network information is incomplete when users or groups move between platforms, and in-group keywords lose significance as public discussion about a group increases. Similarly, using keywords to filter content and users can fail to distinguish between the various groups that discuss a topic—perhaps confounding research on public opinion and narrative trends. We present a classifier intended to distinguish members of groups from users discussing a group based on contextual usage of keywords. We demonstrate the classifier on a sample of community pairs from Reddit and focus on results related to the COVID-19 pandemic.


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When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban
Milo Trujillo | Sam Rosenblatt | Guillermo de Anda Jáuregui | Emily Moog | Briane Paul V. Samson | Laurent Hébert-Dufresne | Allison M. Roth
Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021)

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.