Abuse on the Internet represents a significant societal problem of our time. Previous re-search on automated abusive language detec-tion in Twitter has shown that community-based profiling of users is a promising tech-nique for this task.However, existing ap-proaches only capture shallow properties of online communities by modeling follower–following relationships. In contrast, working with graph convolutional networks (GCNs), we present the first approach that captures not only the structure of online communities but also the linguistic behavior of the users within them. We show that such a heteroge-neous graph-structured modeling of communi-ties significantly advances the current state of the art in abusive language detection.