Homophily - a Driving Factor for Hate Speech on Twitter

In this paper, we hypothesize that homophily is a powerful driver in the generation of hate speech on Twitter. Prior works have shown the role of homophily in information diffusion, sustenance of online guilds and contagion in product adoption. We observe that familiarity computation techniques used in the literature such as the presence of an edge, and the number of mutual friends between a pair of users, have ample scope of improvement. We address the limitations of existing familiarity computation methods by employing a variational-graph-auto-encoder to capture a user's position in the network and utilizing this representation to compute familiarity. We empirically demonstrate the presence of homophily on a dataset from Twitter. Further, we investigate how homophily varies with different hateful forms, such as hate manifesting in topics of gender, race, colour etc. Our results indicate higher homophily in users associating with topics of racism, sexism and nationalism.