Exploring the prevalence of homophily among classes of hate speech

In this paper, we investigate the phenomenon of homophily in hate speech generation on Twitter, aiming to deepen our understanding of online hate dynamics. Given the vast amount of information available on Twitter, computing familiarity and similarity–essential for discovering homophily–poses significant challenges. To address this, we introduce novel measures for computing familiarity and similarity on the platform. Hate speech on social media can manifest in various forms, including hate against gender, race, ethnicity, politics, and nationalism. Consequently, we propose methods to detect multiple forms of hate speech. Utilizing an empirical dataset from Twitter, we demonstrate the prevalence of homophily and explore its variations across different categories of hate speech.