Modeling Group Socialization in Online Communities

Abstract

Newcomers to online communities must learn a group's norms to participate successfully. This process is known as socialization, and has been the subject of considerable study in the online communities literature. Foundational work on group socialization in social psychology, however, suggests that socialization is actually a bidirectional process: individuals and communities adapt to each other's needs. In this work we develop a formal quantitative model of socialization, and present the first large scale empirical validation of the bidirectional socialization hypothesis using Reddit data. More specifically, we focus on linguistic socialization, and model users' comments in online communities as a mixture of in-group language and language from ``background'' communities they previously participated in. On a dataset of 112 subreddits (7.8 million tokens) find that our bidirectional model makes predictions that better generalize across users and time than two unidirectional socialization models. Furthermore, we conduct a qualitative analysis in which we demonstrate that our model is able to extract interpretable socialization patterns from the data that align with the existing literature. Specifically our model is able to learn that users conform more to group norms in topically niche communities with stricter content moderation rules, and that long time users conform more than newcomers. We emphasize that the claims made in this paper are explicitly falsifiable -- we are staking out a clear claim about what the socialization process looks like, and encourage future work to challenge our model.