  Many online hate groups exist to disparage others based on race, gender
identity, sex, or other characteristics. The accessibility of these communities
allows users to join multiple types of hate groups (e.g., a racist community
and misogynistic community), which calls into question whether these
peripatetic users could be further radicalized compared to users that stay in
one type of hate group. However, little is known about the dynamics of joining
multiple types of hate groups, nor the effect of these groups on peripatetic
users. In this paper, we develop a new method to classify hate subreddits, and
the identities they disparage, which we use to better understand how users
become peripatetic (join different types of hate subreddits). The hate
classification technique utilizes human-validated LLMs to extract the protected
identities attacked, if any, across 168 subreddits. We then cluster
identity-attacking subreddits to discover three broad categories of hate:
racist, anti-LGBTQ, and misogynistic. We show that becoming active in a user's
first hate subreddit can cause them to become active in additional hate
subreddits of a different category. We also find that users who join additional
hate subreddits, especially of a different category, become more active in hate
subreddits as a whole and develop a wider hate group lexicon. We are therefore
motivated to train an AI model that we find usefully predicts the hate
categories users will become active in based on post text read and written. The
accuracy of this model may be partly driven by peripatetic users often using
the language of hate subreddits they eventually join. Overall, these results
highlight the unique risks associated with hate communities on a social media
platform, as discussion of alternative targets of hate may lead users to target
more protected identities.
