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

TL;DR

This paper investigates the diffusion of hate speech on Twitter, analyzing user behavior and developing models to predict hate speech initiation and retweet dynamics, with a focus on topic-aware modeling and real-world knowledge integration.

Contribution

It introduces a comprehensive large-scale dataset and proposes novel models, including RETINA, for predicting hate speech diffusion with improved accuracy over existing methods.

Findings

Best hate speech initiation model achieves macro F1 of 0.65.

RETINA outperforms state-of-the-art models with macro F1 of 0.85.

Differentiates hate diffusion dynamics from general information spread.

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 remain under-explored. In this work, we focus on exploring user behaviour, which triggers the genesis of hate speech on Twitter and how it diffuses 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 modelling 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.