Abstract:
Online hate speech, particularly over microblogging platforms like Twitter, has emerged
as arguably the most severe issue of the past decade. Several countries have reported a steep
rise in hate crimes infuriated by malicious hate campaigns. While the detection of hate speech
is one of the emerging research areas, the generation and spread of topic-dependent hate
in the information network remains underexplored. In this work, we focus on exploring
user behavior, which triggers the genesis of hate speech on Twitter and how it diffuse via
retweets. We crawl a large-scale dataset of tweets, retweets, user activity history, and follower
networks, comprising over 161 million tweets from more than 41 million unique users. We
also collect over 600k contemporary news articles published online. We characterize different
signals of information that govern these dynamics. Our analyses differentiate the diffusion
dynamics in the presence of hate from usual information diffusion. This motivates us to
formulate the modeling problem in a topic-aware setting with real-world knowledge. For
predicting the initiation of hate speech for any given hashtag, we propose multiple feature-rich
models, with the best performing one achieving a macro F1 score of 0:65. Meanwhile, to
predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture
that incorporates exogenous influence using scaled dot-product attention. RETINA achieves a
macro F1 score of 0:85, outperforming multiple state-of-the-art models. Our analysis reveals
the superlative power of RETINA to predict the retweet dynamics of hateful content compared
to the existing diffusion models.
