Hate Lingo: A Target-Based Linguistic Analysis of Hate Speech in Social Media

TLDR

While social media empowers freedom of expression, it also facilitates anti‑social behavior, online harassment, cyberbullying, and hate speech. The study aims to deepen understanding of online hate speech by examining its target—whether directed at a specific person or entity or generalized toward a protected group. The authors conduct the first linguistic and psycholinguistic analysis of directed versus generalized hate speech, identifying markers that distinguish the two forms. Directed hate speech is more informal, angrier, and explicitly name‑calls targets with fewer analytic words and more authority‑related terms, whereas generalized hate speech is dominated by religious content, lethal verbs such as murder, exterminate, kill, and quantity words like million and many, providing a data‑driven basis for understanding and detecting online hate.

Abstract

While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech -- its target: either directed towards a specific person or entity, or generalized towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that distinguish these types of hate speech. Our analysis reveals that Directed hate speech, in addition to being more personal and directed, is more informal, angrier, and often explicitly attacks the target (via name calling) with fewer analytic words and more words suggesting authority and influence. Generalized hate speech, on the other hand, is dominated by religious hate, is characterized by the use of lethal words such as murder, exterminate, and kill; and quantity words such as million and many. Altogether, our work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications, but also its detection.