Análise da Percepção das Pessoas no Twitter Sobre Ações Policiais
Resumo
É cada vez mais frequente o uso de redes sociais online como meio para as pessoas trocarem ideias e expressarem suas opiniões sobre diferentes aspectos do cotidiano, incluindo violência e insegurança, um problema central a vários centros urbanos. Neste artigo investigamos o potencial uso de comentários compartilhados em uma rede social bastante popular – o Twitter – para inferir a opinião pública sobre a atuação policial em incidentes de segurança de grande repercussão. Nesse sentido, exploramos atributos extraídos desses comentários e modelos de aprendizado de máquina para inferir o posicionamento das pessoas em relação a ações policiais específicas. Nossos experimentos mostram quão desafiante é essa inferência dado grande quantidade de neutralidade e sarcasmo observado em mídias sociais. Não obstante, nossos melhores classificadores alcançaram acurácia e especificidade (macro F1) superiores a 68% para inferir posicionamentos de aprovação, desaprovação e neutralidade da população.
Palavras-chave:
BERT, transformers, policia, Twitter, opinião
Referências
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Chaparro, L. F., Pulido, C., Rudas, J., Reyes, A. M., Victorino, J., Narváez, L. Á., Gómez, F., and Martinez, D. (2020). Sentiment analysis of social network content to characterize the perception of security. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 685–691. IEEE.
Chen, X., Cho, Y., and Jang, S. Y. (2015). Crime prediction using twitter sentiment and weather. In Systems and Information Engineering Design Symposium, pages 63–68. IEEE.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Association for Computational Linguistics: Human Language Technologies, pages 4171–4186.
Ferreira, C. H., Murai, F., Silva, A. P., Almeida, J. M., Trevisan, M., Vassio, L., Mellia, M., and Drago, I. (2021). On the dynamics of political discussions on instagram: A network perspective. Online Social Networks and Media, 25:100155.
Glandt, K., Khanal, S., Li, Y., Caragea, D., and Caragea, C. (2021). Stance detection in covid-19 tweets. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, volume 1.
Hand, L. C. and Ching, B. D. (2020). Maintaining neutrality: A sentiment analysis of police agency facebook pages before and after a fatal officer-involved shooting of a citizen. Government Information Quarterly, 37(1):101420.
Hossain, T., Logan IV, R. L., Ugarte, A., Matsubara, Y., Young, S., and Singh, S. (2020). Covidlies: Detecting covid-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020.
Iranmanesh, A. and Alpar Atun, R. (2020). Reading the urban socio-spatial network through space syntax and geo-tagged twitter data. Journal of Urban Design, 25(6):738–757.
Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9:381–386.
Malagoli, L. G., Stancioli, J., Ferreira, C. H., Vasconcelos, M., Couto da Silva, A. P., and Almeida, J. M. (2021). A look into covid-19 vaccination debate on twitter. In ACM Web Science Conference 2021.
Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., and Gao, J. (2021). Deep learning– based text classification: a comprehensive review. ACM Computing Surveys (CSUR), 54(3):1–40.
Mozafari, M., Farahbakhsh, R., and Crespi, N. (2019). A bert-based transfer learning approach for hate speech detection in online social media. In International Conference on Complex Networks and Their Applications, pages 928–940. Springer.
NEV-USP (2021). Monitor da violência. Disponível em: [link]. Acesso em 07 de jun. 2021.
Nobre, G. P., Ferreira, C. H., and Almeida, J. M. (2022). A hierarchical network-oriented analysis of user participation in misinformation spread on whatsapp. Information Processing & Management, 59(1):102757.
Oglesby-Neal, A., Tiry, E., and Kim, K. (2019). Public perceptions of police on social media. Washington, DC: Urban Institute.
Reimers, N., Gurevych, I., Reimers, N., Gurevych, I., Thakur, N., Reimers, N., Daxenberger, J., Gurevych, I., Reimers, N., Gurevych, I., et al. (2019). Sentence-bert: Sentence embeddings using siamese bertnetworks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics.
Reis, J., de Souza, F., de Melo, P. V., Prates, R., Kwak, H., and An, J. (2015). Breaking the news: First impressions matter on online news. In Proceedings of the International AAAI Conference on Web and Social Media, volume 9, pages 357–366.
Sousa, D. d. S., Feitosa, M. P. F., and Gonçalves, G. D. (2021). Relações entre crimes e o espaço urbano: Um estudo de caso baseado em pontos de interesses extraídos da web. In Anais do V Workshop de Computação Urbana, pages 196–208. SBC.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In Brazilian Conference on Intelligent Systems.
Tucker, R., O’Brien, D. T., Ciomek, A., Castro, E., Wang, Q., and Phillips, N. E. (2021). Who ‘tweets’ where and when, and how does it help understand crime rates at places? measuring the presence of tourists and commuters in ambient populations. Journal of Quantitative Criminology, 37(2):333–359.
Wang, H., Yao, H., Kifer, D., Graif, C., and Li, Z. (2017). Non-stationary model for crime rate inference using modern urban data. IEEE transactions on big data, 5(2):180–194.
Wang, X., Gerber, M. S., and Brown, D. E. (2012). Automatic crime prediction using events extracted from twitter posts. In International conference on social computing, behavioral-cultural modeling, and prediction, pages 231–238. Springer.
Weinzierl, M., Hopfer, S., and Harabagiu, S. (2021). Misinformation adoption or rejection in the era of covid-19. In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), AAAI Press.
Publicado
31/07/2022
Como Citar
FEITOSA, Marcos Paulo Fontes; FERREIRA, Carlos H. G.; GONÇALVES, Glauber Dias; ALMEIDA, Jussara Marques de.
Análise da Percepção das Pessoas no Twitter Sobre Ações Policiais. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 11. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 73-84.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2022.223267.
