Consumption of Hate Speech on Twitter - A Topical Approach to Capture Networks of Hateful Users.

In this paper, we attempt to track the dissemination of hate speech on Twitter. We argue that hate is not a blanket category but exists across multiple topics. We use topic modelling to unearth the latent topics in tweets and an ensemble classification model to capture various nuances of hate speech. We further validate our approach by manually annotating 4, 720 tweets. On analysing the mechanisms of hate speech dissemination, we find that hateful tweets garner 2.4 times more retweets than non-hateful tweets. Further, the retweet network allows us to define topical inflow and outflow vectors which we use to classify users as originators, propagators, and constrictors. We then examine how these users take part in the dissemination of topical information and observe considerable differences in how users associate with hateful and non-hateful topics. Furthermore, the retweet network enables us to analyse the structure of the strongest connected components for different topics. We find some topics associate strongly with hate and users associated with these topics have high degree centrality, are densely connected, and have a large strongly connected core.