The growing prominence of online hate speech is a threat to a safe and just society. This endangering phenomenon requires collaboration across the sciences in order to generate evidence-based knowledge of, and policies for, the dissemination of hatred in online spaces. To foster such collaborations, here we present the Gab Hate Corpus (GHC), consisting of 27,665 posts from the social network service gab.ai, each annotated by a minimum of three trained annotators. Annotators were trained to label posts according to a coding typology derived from a synthesis of hate speech definitions across legal, computational, psychological, and sociological research. We detail the development of the corpus, describe the resulting distributions of hate-based rhetoric, target group, and rhetorical framing labels, and establish baseline classification performance for each using standard natural language processing methods. The GHC, which is the largest theoretically-justified, annotated corpus of hate speech to date, provides opportunities for training and evaluating hate speech classifiers and for scientific inquiries into the linguistic and network components of hate speech.

Acts of hate have been used to silence, terrorize, and erase marginalized social groups throughout history. The rising rates of these behaviors in recent years underscores the importance of developing a better understanding of when, why, and where they occur.In this work, we present a program of research that suggests that acts of hate may often be best understood not just as responses to threat, but also as morally motivated behaviors grounded in people's moral values and perceptions of moral violations. As evidence for this claim, we present findings from five studies that rely on a combination of natural language processing, spatial modeling, and experimental methods to investigate the relationship between moral values and acts of hate toward marginalized groups. Across these studies, we find consistent evidence that moral values oriented around ingroup preservation are disproportionately evoked in hate speech, predictive of the county-level prevalence of hate groups, and associated with the belief that acts of hate against marginalized groups are justified. Additional analyses suggest that the association between group-oriented moral values and hate acts against marginalized groups can be partly explained by the belief that these groups have done something morally wrong. By accounting for the role of moralization in acts of hate, this work provides a unified framework for understanding hateful behaviors and the events or dynamics that trigger them.

Online radicalization is among the most vexing challenges the world faces today. Here, we demonstrate that homogeneity in moral concerns results in increased levels of radical intentions. In Study 1, we find that in Gab – a right-wing extremist network – the degree of moral convergence within a cluster, predicts the number of hate-speech messages members post. In Study 2, we replicate this effect in another extremist network; Incels. In Study 3 (N = 333), we demonstrate that experimentally leading people to believe that others in their group share their moral views increases their radical intentions. Study 4 (N = 510) replicates this effect in a stratified representative sample, and finds that this causal link may be explained by the degree to which individuals’ identities are fused with their ingroup. Our findings highlight the role of moral convergence and identity fusion in radicalization, emphasizing the need for diversity of moral worldviews within social networks.