Capturing the Spread of Hate on Twitter Using Spreading Activation Models

Seema Nagar, Sameer Gupta, Ferdous Ahmed Barbhuiya, Kuntal Dey

Abstract: Hate speech is a prevalent and pervasive phenomenon on social media platforms. Detecting hate speech and modelling its spread is a significant research problem with practical implications. Though hate speech detection has been an active area of research, hate spread modelling is still in its nascent stage. Prior works have analyzed the hateful users’ social network embedding, hateful user detection using belief propagation models, and the spread velocity of hateful content. However, these prior works fail to factor in the multiple hateful forms (such as hate against gender, race and ethnicity) and the temporal evolution of hate spread, limiting their applicability. We take a holistic approach wherein we model the spread of hate as a single form and fine-granular spread of hateful forms. We extend the traditional spread and activation (SPA) model to capture the spread of hate and its forms. We use SPA to model, the spread of hate as one single form while TopSPA captures the spread of multiple hate forms. We also propose ways to detect hateful forms by using latent topics present in hateful content. We empirically demonstrate our approach to a dataset from Twitter that contains ample hate speech instances along with users labelled as hateful or not.