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Online hate speech, particularly over microblogging platforms like Twitter,has emerged as arguably the most severe issue of the past decade. Severalcountries have reported a steep rise in hate crimes infuriated by malicioushate campaigns. While the detection of hate speech is one of the emergingresearch areas, the generation and spread of topic-dependent hate in theinformation network remain under-explored. In this work, we focus on exploringuser behaviour, which triggers the genesis of hate speech on Twitter and how itdiffuses via retweets. We crawl a large-scale dataset of tweets, retweets, useractivity history, and follower networks, comprising over 161 million tweetsfrom more than $41$ million unique users. We also collect over 600kcontemporary news articles published online. We characterize different signalsof information that govern these dynamics. Our analyses differentiate thediffusion dynamics in the presence of hate from usual information diffusion.This motivates us to formulate the modelling problem in a topic-aware settingwith real-world knowledge. For predicting the initiation of hate speech for anygiven hashtag, we propose multiple feature-rich models, with the bestperforming one achieving a macro F1 score of 0.65. Meanwhile, to predict theretweet dynamics on Twitter, we propose RETINA, a novel neural architecturethat incorporates exogenous influence using scaled dot-product attention.RETINA achieves a macro F1-score of 0.85, outperforming multiplestate-of-the-art models. Our analysis reveals the superlative power of RETINAto predict the retweet dynamics of hateful content compared to the existingdiffusion models.

Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter