Hate Speech Detection: Homophobia
Abstract
Understanding the context and reasons for homophobic actions is primordial to mitigate this type of hatred. This work presents the construction of a dataset taken from Twitter, which contains information on homophobic discourses. The contributions are: (1) the method of building a dataset and anonymous labeling based on their homophobic content; (2) the creation of features of this dataset; (3) the evaluation of machine learning methods for the classification of data regarding homophobic content. Preliminary results show that the Random Forest Classifier model stands out in the identification of homophobic tweets with F1-score of 0.8, recall of 0.9 and precision of 0.7.
Keywords:
homophobia, hate speech, data science
References
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Amper (2021). We are social e hootsuite digital 2021 [resumo e relatório completo]. https://tinyurl.com/we-are-social-e-hootsuite.
Bermingham, A. and Smeaton, A. (2011). On using Twitter to monitor political sentiment and predict election results. In Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011), pages 2–10, Chiang Mai, Thailand. Asian Federation of Natural Language Processing.
Burnap, P. and Williams, M. L. (2015). Cyber hate speech on twitter: An application of machine classification and statistical modeling for policy and decision making. Policy & Internet, 7(2):223–242.
Cazelatto, C. and Cardin, V. S. G. (2016). O discurso de ódio homofóbico no brasil: um instrumento limitador da sexualidade humana. Revista Jurídica Cesumar-Mestrado, 16.
Chamberland and Lebreton (2012). Réflexions autour de la notion d’homophobie : succés politique, malaises conceptuels et application empirique. Nouv Questions Feministes.
Chambers, S. A. (2007). An incalculable effect’: Subversions of heteronormativity. Political studies, 55.
Council, H. R. (2013). Human rights. annual report of the united nations high commissioner for human rights. https://tinyurl.com/rabat-united-nations.
Davidson, T., Warmsley, D., Macy, M. W., and Weber, I. (2017). Automated hate speech detection and the problem of offensive language. CoRR, abs/1703.04009.
Frässé, C. and Barrientos, J. (2016). The concept of homophobia: A psychosocial perspective. Sexologies, 25.
Kwok, I. and Wang, Y. (2013). Locate the hate: Detecting tweets against blacks. In AAAI.
Miriam J., M. M. (2002). The trivium: The liberal arts of logic, grammar, and rhetoric : understanding the nature and function of language. Paul Dry Books.
Pereira-Kohatsu, J. C., Quijano-Sánchez, L., Liberatore, F., and Camacho-Collados, M. (2019). Detecting and monitoring hate speech in twitter. Sensors, 19(21).
Statista (2021). Leading countries based on number of twitter users as of october 2021. https://tinyurl.com/statista-twitter-users.
Watanabe, H., Bouazizi, M., and Ohtsuki, T. (2018). Hate speech on twitter: A pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access, 6:13825–13835.
Weinberg, G. H. (1975). Society and the healthy homosexual / [by] George Weinberg. Gerrards Cross, Smythe.
Published
2022-07-31
How to Cite
SOUZA, Andrey O.; NAKAMURA, Eduardo F.; NAKAMURA, Fabíola G..
Hate Speech Detection: Homophobia. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 16. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 73-80.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2022.223222.
