LGBTfobia em Aplicativos de Mensageria Instantânea: Uma Analise por Redes de Co-compartilhamento
Resumo
A LGBTfobia no ambiente digital tem sido investigada predominantemente em plataformas abertas, onde interações explícitas facilitam a modelagem de redes. Em aplicativos de mensageria instantânea, contudo, a circulação de conteúdos ocorre de forma mais opaca, exigindo estratégias específicas de análise. Este trabalho propõe um método para modelar a dinâmica do compartilhamento de mensagens LGBTfóbicas no WhatsApp e no Telegram por meio de redes de co-compartilhamento. Utilizamos o dicionário PrejudicePT-br para selecionar as mensagens de interesse e construímos uma rede usuário–usuário com arestas ponderadas indicando o co-compartilhamento de mensagens idênticas. A estrutura resultante foi avaliada por meio de medidas globais, componentes conexas e detecção de comunidades, complementadas por uma análise qualitativa que distingue gatilhos lexicais ambíguos de termos pejorativos explícitos. Os resultados revelam um padrão global fragmentado, com um núcleo coeso sustentado por um conjunto reduzido de mensagens. O estudo oferece um pipeline replicável e evidências empíricas sobre a estrutura da circulação de conteúdo LGBTfóbico em aplicativos de mensageria instantânea.
Palavras-chave:
LGBTfobia, WhatsApp, Telegram, Redes Complexas, Coordenação
Referências
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Batagelj, V. and Zaveršnik, M. (2003). An o(m) algorithm for cores decomposition of networks. arXiv preprint cs/0310049.
Bird, S., Klein, E., and Loper, E. (2009). Natural language processing with python. O’Reilly Media.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008.
Bozhidarova, M. et al. (2023). Hate speech and hate crimes: A data-driven study of evolving discourse around marginalized groups. In Proc. IEEE International Conference on Big Data.
Cinelli, M., Cresci, S., Quattrociocchi, W., Tesconi, M., and Zola, P. (2022). Coordinated inauthentic behavior and information spreading on Twitter. Decision Support Systems, 160:113819.
de Sá, I. C., Galic, L., Franco, W., Gadelha, T., Monteiro, J. M., and Machado, J. C. (2023). BATMAN: A big data platform for misinformation monitoring. In Filipe, J., Smialek, M., Brodsky, A., and Hammoudi, S., editors, Proceedings of the 25th International Conference on Enterprise Information Systems, ICEIS 2023, Volume 1, Prague, Czech Republic, April 24-26, 2023, pages 237–246. SCITEPRESS.
Fan, L. et al. (2024). Characterizing online toxicity during the 2022 Mpox outbreak: A computational analysis of topical and network dynamics. arXiv preprint.
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1):35–41.
Herek, G. M. (2004). Beyond “homophobia”: Thinking about sexual prejudice and stigma in the twenty-first century. Sexuality Research & Social Policy, 1(2):6–24.
Kansaon, D. et al. (2025). From fake news to real protests: WhatsApp’s role in brazilian political coordination. In Proc. Int. AAAI Conf. on Web and Social Media (ICWSM).
Leite, J. A., Silva, D., Bontcheva, K., and Scarton, C. (2020). Toxic language detection in social media for Brazilian Portuguese: New dataset and multilingual analysis. In Proc. 1st Conf. of the Asia-Pacific Chapter of the ACL and 10th IJCNLP, pages 914–924.
Lima-Lopes, R. E. d. (2025). Lexical patterns of religious conservatism: A study of social media reactions to an art exhibition in Brazil. Digital Studies / Le champ numérique, 15(1).
Machado, C., Kira, B., Narayanan, V., Kollanyi, B., and Howard, P. N. (2019). A study of misinformation in WhatsApp groups with a focus on the Brazilian presidential elections. In Companion Proc. The Web Conference (WWW), pages 1–4.
Newman, M. E. J. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2):026113.
Pacheco, D., Hui, P.-M., Torres-Lugo, C., Truong, B. T., Flammini, A., and Menczer, F. (2021). Uncovering coordinated networks on social media: Methods and case studies. In Proc. Int. AAAI Conf. on Web and Social Media (ICWSM), volume 15, pages 455–466.
SaferNet Brasil (2025). Indicadores da central nacional de denúncias de crimes cibernéticos – 2024. Technical report, SaferNet Brasil.
Sousa, M., do Nascimento, F. F., Martins, G., Monteiro, J. M., and Machado, J. C. (2025a). An approach for the automatic detection of prejudice in instant messaging applications. In Proceedings of the 14th International Conference on Data Science, Technology and Applications (DATA 2025) - Volume 1: DATA, pages 108–119, Bilbao, Spain. INSTICC, SciTePress.
Sousa, M., Nascimento, F., Martins, G., Monteiro, J. M., and Machado, J. (2025b). An approach for the automatic detection of prejudice in instant messaging applications. In Proc. DATA 2025.
Unlu, A. et al. (2025). Mapping the terrain of hate: Identifying and analyzing online communities and political parties engaged in hate speech. In International Journal of Data Science and Analytics.
Vargas, F., Carvalho, I., Rodrigues de Goes, F., Pardo, T., and Benevenuto, F. (2022). HateBR: A large expert annotated corpus of Brazilian Instagram comments for offensive language and hate speech detection. In Proc. 13th Language Resources and Evaluation Conference (LREC), pages 7174–7183.
Vargas, F., Rodrigues de Goes, F., Carvalho, I., Benevenuto, F., and Pardo, T. (2021). Contextual-lexicon approach for abusive language detection. In Proc. RANLP 2021, pages 1438–1447.
Warner, M. (1991). Introduction: Fear of a queer planet. Social Text, (29):3–17.
Publicado
08/09/2026
Como Citar
NASCIMENTO, Fernanda Ferreira do; MONTEIRO, José Maria; MACHADO, Javam.
LGBTfobia em Aplicativos de Mensageria Instantânea: Uma Analise por Redes de Co-compartilhamento. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 289-302.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2026.249212.
