Abuse on the Internet represents a significant societal problem of our time . Previous research on automated abusive language detection in Twitter has shown that communitybased profiling of users is a promising technique for this task . However , existing approaches only capture shallow properties of online communities by modeling followerfollowing 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 . Matthew Zook ( 2012 ) carried out an interesting study showing that the racist tweets posted in response to President Obama 's re-election were not distributed uniformly across the United States but instead formed clusters . This phenomenon is known as homophily : i.e. , people , both in real life and online , tend to cluster with those who appear similar to themselves . To model homophily , recent research in abusive language detection on Twitter ( Mishra et al . , 2018a ) incorporates embeddings for authors ( i.e. , users who have composed tweets ) that encode the structure of their surrounding communities . The embeddings ( called author profiles ) are generated by applying a node embedding framework to an undirected unlabeled community graph where nodes denote the authors and edges the follower-following relationships amongst them on Twitter . However , these profiles do not capture the linguistic behavior of the authors and their communities and do not convey whether their tweets tend to be abusive or not . In contrast , we represent the community of authors as a heterogeneous graph consisting of two types of nodes , authors and their tweets , rather than a homogeneous community graph of authors only . The primary advantage of such heterogeneous representations is that they enable us to model both community structure as well as the linguistic behavior of authors in these communities . To generate richer author profiles , we then propose a semi-supervised learning approach based on graph convolutional networks ( GCNs ) applied to the heterogeneous graph representation . To the best of our knowledge , our work is the first to use GCNs to model online communities in social media . We demonstrate that our methods provide significant improvements over existing techniques . In this paper , we built on the work of Mishra et al . ( 2018a ) that introduces community-based profiling of authors for abusive language detection . We proposed an approach based on graph convolutional networks to show that author profiles that directly capture the linguistic behavior of authors along with the structural traits of their community significantly advance the current state of the art .

Challenge: Existing approaches to abusive language detection in Twitter only capture shallow follower-following relationships to model homophily by a node embedding framework without considering linguistic behavior.
Approach: They propose a graph convolutional network-based approach which captures the community structure linguistic behaviors by representing it as a heterogeneous graph of authors and tweets.
Outcome: The proposed model outperforms the current state-of-the-art techniques in abusive language detection.