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

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 behavior, 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 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 stateof-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.

Hatemongers ride on echo chambers to escalate hate speech diffusion

“…In SI Appendix (Figure 12), we show the distribution of hatefulness and cascade volume for different most-occurring topics across the three platforms. It can be readily seen that the point of hatefulness concentration (low, medium, or high) varies across different topics for different platforms, pointing towards the topical dependence of hateful behavior observed in prior studies [28]. For topics related to anti-abortion or pro-life, we see that the distribution obtains a peak in Gab but the same is not observed for Reddit.…”

“…Multiple meta-analyses have suggested a superlinear growth in research related to hate speech in recent years [50,33,13]. Most of these studies seek to identify hate speech; some explore the dynamics as well [46,45,28,26]. The latter is particularly of interest for combating the spread of hate speech since only content moderation via flagging, banning, or deleting posts may not be enough in this context [7,39,26] (it may often incur threats to the democratic principles [37]).…”

“…Two separate earlier findings in this context prepare the foundation of our current study. Firstly, the diffusion of information over a platform, whether mediated by hateful actors (users) or via hateful content (posts), ex-hibits different characteristics compared to their non-hateful counterparts [13,28]. Secondly, in the social science community, it has been conjectured that hateful and extremist behavior might be linked with the formation of echo chambers [22,44] -groups of users who share a strong opinion align themselves in the interaction network in such a way that they are exposed to content correlated to their chosen ideology.…”

Handling Bias in Toxic Speech Detection: A Survey

“…Such behaviour manifests in content (text, image, audio-video) aimed at harming individuals or groups based on personal attributes such as race, gender, and ethnicity. At extreme online, hate speech (a type of toxic speech) [50] can lead to incidents of offline violence causing loss of life, and property [25,51]. The real-world impact of toxic online content and its multifaceted nature have galvanised academic and industrial research aimed at early detection and mitigation of such content.…”

Measuring Social Solidarity During Crisis: The Role of Design Choices

“…Second, focusing on the current COVID-19 pandemic, research has mainly investigated changes in anti-social online behavior, such as the rise of hate speech, documenting increasing defamation against a wide range of individuals and groups, including immigrants and refugees [14,15] . Whereas an increase in anti-social online behavior is certainly worrying [16] , it remains unclear whether it indeed reflects growing levels of onesided hostility in society towards social groups or rather heightened issue salience.…”