  Abuse on the Internet represents a significant societal problem of our time.
Previous research on automated abusive language detection in Twitter has shown
that community-based profiling of users is a promising technique for this task.
However, existing approaches 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 heterogeneous
graph-structured modeling of communities significantly advances the current
state of the art in abusive language detection.
