Abstract
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.
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Notes
- 1.
Sexism or gender discrimination is prejudice and, sometimes, discrimination based on a person’s gender or sex.
- 2.
Available in https://github.com/brendasalenave/sexist_hate_speech.
- 3.
- 4.
Pejorative reference to a person who is affiliated to the Brazilian Worker’s Party, i.e., Partido dos Trabalhadores.
- 5.
Available in: https://www.perspectiveapi.com.
- 6.
There are additional metrics offered for other languages.
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This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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Santana, B.S., Vanin, A.A., Wives, L.K. (2022). Sexist Hate Speech: Identifying Potential Online Verbal Violence Instances. In: Pinheiro, V., 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
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