Monitorando a Opinião Pública sobre Operações Policiais no Brasil via Comentários de Vídeos no YouTube
Resumo
Neste trabalho, propomos um sistema que utiliza comentários de usuários no YouTube para monitorar a percepção das pessoas sobre operações policiais em incidentes de violência urbana com repercussão nessa plataforma. Exploramos atributos extraídos desses comentários e modelos de processamento de linguagem natural, mostrando os desafios dessa inferência ao longo de dois anos. Nossos melhores modelos alcançaram acurácia e macro-F1 de 87% para inferir posicionamentos de aprovação, desaprovação e neutralidade, além de uma boa capacidade de generalização em diferentes plataformas, avaliada no Twitter/X e YouTube. Como resultados identificamos períodos com posicionamentos dominantes, que desconsiderando neutralidade, tendem majoritariamente à aprovação das operações policiais, ao passo que desaprovações foram identificadas em granularidade regional.
Referências
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Brainard, L. and Edlins, M. (2015). Top 10 us municipal police departments and their social media usage. The American Review of Public Administration, 45(6):728–745.
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D’Andrea, E., Ducange, P., Bechini, A., Renda, A., and Marcelloni, F. (2019). Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Systems with Applications, 116:209–226.
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Feitosa, M. P. F., Ferreira, C. H., Gonçalves, G. D., and de Almeida, J. M. (2022). Análise da percepção das pessoas no twitter sobre ações policiais. In Anais do XI Brazilian Workshop on Social Network Analysis and Mining, pages 73–84. SBC.
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.
Junior, J. M. d. R., Linhares, R. S., Ferreira, C. H. G., Nobre, G. P., Murai, F., and Almeida, J. M. (2022). Uncovering discussion groups on claims of election fraud from twitter. In International Conference on Social Informatics, pages 320–336. Springer.
Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Bielaniewicz, J., Gruza, M., Janz, A., Kanclerz, K., et al. (2023). Chatgpt: Jack of all trades, master of none. Information Fusion, 99:101861.
Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9:381–386.
Mohammad, S. M., Sobhani, P., and Kiritchenko, S. (2017). Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT), 17(3):1–23.
NEV-USP (2024). Monitor da violência. Disponível em: [link]. Acesso em 07 de mar. 2024.
Newman, N., Fletcher, R., Robertson, C. T., Eddy, K., and Nielsen, R. K. (2022). Reuters institute digital news report 2022. Reuters Institute for the study of Journalism.
Ricardo, C. d. M., de Siqueira, P. P., and Marques, C. R. (2013). Estudo conceitual sobre os espaços urbanos seguros. Revista brasileira de segurança pública, 7(1):200–216.
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, M., Wu, H., Zhang, T., and Zhu, S. (2020). Identifying critical outbreak time window of controversial events based on sentiment analysis. Plos one, 15(10):e0241355.
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.
Brainard, L. and Edlins, M. (2015). Top 10 us municipal police departments and their social media usage. The American Review of Public Administration, 45(6):728–745.
Brown, G. R. (2016). The blue line on thin ice: Police use of force modifications in the era of cameraphones and youtube. British journal of criminology, 56(2):293–312.
Ceron, A. and Negri, F. (2016). The “social side” of public policy: Monitoring online public opinion and its mobilization during the policy cycle. Policy & Internet, 8(2):131–147.
Chakraborty, P. and Sharma, A. (2019). Public opinion analysis of the transportation policy using social media data: a case study on the delhi odd–even policy. Transportation in Developing Economies, 5:1–9.
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.
D’Andrea, E., Ducange, P., Bechini, A., Renda, A., and Marcelloni, F. (2019). Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Systems with Applications, 116:209–226.
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.
El Barachi, M., AlKhatib, M., Mathew, S., and Oroumchian, F. (2021). A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change. Journal of Cleaner Production, 312:127820.
Feitosa, M. P. F., Ferreira, C. H., Gonçalves, G. D., and de Almeida, J. M. (2022). Análise da percepção das pessoas no twitter sobre ações policiais. In Anais do XI Brazilian Workshop on Social Network Analysis and Mining, pages 73–84. SBC.
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.
Junior, J. M. d. R., Linhares, R. S., Ferreira, C. H. G., Nobre, G. P., Murai, F., and Almeida, J. M. (2022). Uncovering discussion groups on claims of election fraud from twitter. In International Conference on Social Informatics, pages 320–336. Springer.
Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Bielaniewicz, J., Gruza, M., Janz, A., Kanclerz, K., et al. (2023). Chatgpt: Jack of all trades, master of none. Information Fusion, 99:101861.
Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9:381–386.
Mohammad, S. M., Sobhani, P., and Kiritchenko, S. (2017). Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT), 17(3):1–23.
NEV-USP (2024). Monitor da violência. Disponível em: [link]. Acesso em 07 de mar. 2024.
Newman, N., Fletcher, R., Robertson, C. T., Eddy, K., and Nielsen, R. K. (2022). Reuters institute digital news report 2022. Reuters Institute for the study of Journalism.
Ricardo, C. d. M., de Siqueira, P. P., and Marques, C. R. (2013). Estudo conceitual sobre os espaços urbanos seguros. Revista brasileira de segurança pública, 7(1):200–216.
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, M., Wu, H., Zhang, T., and Zhu, S. (2020). Identifying critical outbreak time window of controversial events based on sentiment analysis. Plos one, 15(10):e0241355.
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
21/07/2024
Como Citar
ROCHA, Saul Sousa da; SILVA, Carlos Henrique do Vale e; FERREIRA, Carlos H. G.; GONÇALVES, Glauber Dias; ALMEIDA, Jussara Marques de.
Monitorando a Opinião Pública sobre Operações Policiais no Brasil via Comentários de Vídeos no YouTube. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 13. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 158-171.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2024.3101.
