Using Sentiment Induction to Understand Variation in Gendered Online Communities
This research addresses the problem of understanding gender variation in online communities for social scientists and platform analysts, but it is incremental as it builds on existing methods for community analysis.
The study analyzed gendered communities on Reddit using text, user, and sentiment representations, finding that these representations reveal distinct community identities and that sentiment lexicons can indicate social meaning and values, with results showing that social platforms are active settings for different constructions of gender.
We analyze gendered communities defined in three different ways: text, users, and sentiment. Differences across these representations reveal facets of communities' distinctive identities, such as social group, topic, and attitudes. Two communities may have high text similarity but not user similarity or vice versa, and word usage also does not vary according to a clearcut, binary perspective of gender. Community-specific sentiment lexicons demonstrate that sentiment can be a useful indicator of words' social meaning and community values, especially in the context of discussion content and user demographics. Our results show that social platforms such as Reddit are active settings for different constructions of gender.