Sexist Hate Speech: Identifying Potential Online Verbal Violence Instances

Online communication provides space for content dissemination and opinion sharing. However, the limit between opinion and offense might be exceeded, characterizing hate speech. Moreover, its automatic detection is challenging, and approaches focused on the Portuguese language are scarce. This paper proposes an interface between linguistic concepts and computational interventions to support hate speech detection. We applied a Natural Language Processing pipeline involving topic modeling and semantic role labeling, allowing a semi-automatic identification of hate speech. We also discuss how such speech qualifies as a type of verbal violence widespread on social networks to reinforce a sexist stereotype. Finally, we use Twitter data to analyze information that resulted in virtual attacks against a specific person. As an achievement, this work validates the use of linguistic features to annotate data either as hate speech or not. It also proposes using fallacies as a potential additional feature to identify potential intolerant discourses.

Download paper here

Recommended citation: Santana, B.S., Vanin, A.A., Wives, L.K. (2022). Sexist Hate Speech: Identifying Potential Online Verbal Violence Instances. In: , et al. Computational Processing of the Portuguese Language. PROPOR 2022. Lecture Notes in Computer Science(), vol 13208. Springer, Cham. https://doi.org/10.1007/978-3-030-98305-5_17