Abusive Language Detection with Graph Convolutional Networks
This addresses the societal problem of online abuse for social media platforms and users, with an incremental advance over previous community-based methods.
The paper tackled abusive language detection on Twitter by modeling both community structure and user linguistic behavior using graph convolutional networks, achieving significant state-of-the-art improvements.
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.