STALLA: Um Framework para Análise de Fontes Abertas durante a Pandemia do Covid-19
Resumo
A expansão das redes sociais resultou em um aumento na distribuição de campanhas de desinformação, que colocam em risco a estabilidade democrática nacional, tornando-se um elemento desfavorável para a produção do conhecimento de Inteligência. Com o objetivo de mitigar este óbice, foi proposto o framework STALLA para coleta, tratamento, rotulação automatizada e análise de informações, proporcionando maior eficiência na produção do conhecimento. Assim, o estudo tem por escopo a pandemia do Covid-19, a partir de dados coletados de textos curtos (tweets), no idioma português, da rede social Twitter. Considerando-se os trabalhos correlatos, as Redes Neurais Recorrentes (RNN) apresentam-se como as mais vocacionadas para análises textuais. A partir dessa premissa, o desempenho do STALLA foi analisado comparando-se as implementações das redes LSTM e BiLSTM, resultando em uma acurácia de aproximadamente 70%.
Referências
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Pérez-Rosas, Verónica; Mihalcea, R. (2015). Experiments in open domain deception detection. In Proceedings of the 2015 conference on empirical methods in natural language processing. 2015. p. 1120-1125.
Rashkin, H. e. a. (2017). Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 conference on empirical methods in natural language processing. 2017. p. 2931-2937.
Ratner, A. J. e. a. (2017). Snorkel: Fast training set generation for information extraction. In Proceedings of the 2017 ACM international conference on management of data. 2017. p. 1683-1686.
Rubin, V. L. e. a. (2016). Fake news or truth? using satirical cues to detect potentially misleading news. In Proceedings of the second workshop on computational approaches to deception detection. 2016. p. 7-17.
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Vosoughi, Soroush; Roy, D. A. S. (2018). The spread of true and false news online. In Science, v. 359, n. 6380, p. 1146-1151, 2018.
Weigang, L., Enamoto, L. M., Li, D. L., and Rocha Filho, G. P. (2022). New directions for artificial intelligence: human, machine, biological, and quantum intelligence. Frontiers of Information Technology & Electronic Engineering, 23(6):984-990.
Appling, Darren Scott; Briscoe, E. J. H. C. J. (2015). Discriminative models for predicting deception strategies. In Proceedings of the 24th International Conference on World Wide Web, pages 947-952.
Bechmann, Anja; Nielbo, K. L. (2018). Are we exposed to the same "news" in the news feed? an empirical analysis of filter bubbles as information similarity for danish facebook users. In Digital journalism, v. 6, n. 8, pages 990-1002.
Brasil (2017). Decreto nº 8.793, de 29 de junho de 2016. aprova a política nacional de inteligência. In Diário Oficial da República Federativa do Brasil, Brasília, DF, Edição 241, 18 dez. 2017. Seção 1, p. 36-39.
Duran, N. D. e. a. (2010). The linguistic correlates of conversational deception: Comparing natural language processing technologies. In Applied Psycholinguistics, v. 31, n. 3, p. 439-462, 2010.
Enamoto, L., Santos, A. R., Maia, R., Weigang, L., and Filho, G. P. R. (2022). Multi-label legal text classification with bilstm and attention. International Journal of Computer Applications in Technology, 68(4):369-378.
Enamoto, L., Weigang, L., and Filho, G. P. R. (2021). Generic framework for multilingual short text categorization using convolutional neural network. Multimedia Tools and Applications, 80:13475-13490.
Hauch, V. e. a. (2015). Are computers effective lie detectors? a meta-analysis of linguistic cues to deception. In Personality and social psychology Review, v. 19, n. 4, p. 307-342, 2015.
Kang, Cecilia; Goldman, A. (2016). In washington pizzeria attack, fake news brought real guns. In New York Times, v. 5, 2016.
Lazer, D. M. e. a. (2018). The science of fake news. In Science, v. 359, n. 6380, p. 1094-1096, 2018.
Nakashole, Ndapandula; Mitchell, T. (2014). Language-aware truth assessment of fact candidates. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2014. p. 1009-1019.
Oshikawa, Ray; Qian, J. W. W. Y. (2020). A survey on natural language processing for fake news detection. In Conference on Language Resources and Evaluation (LREC 2020), pages 6086-6093.
Pérez-Rosas, Verónica; Mihalcea, R. (2015). Experiments in open domain deception detection. In Proceedings of the 2015 conference on empirical methods in natural language processing. 2015. p. 1120-1125.
Rashkin, H. e. a. (2017). Truth of varying shades: Analyzing language in fake news and political fact-checking. In Proceedings of the 2017 conference on empirical methods in natural language processing. 2017. p. 2931-2937.
Ratner, A. J. e. a. (2017). Snorkel: Fast training set generation for information extraction. In Proceedings of the 2017 ACM international conference on management of data. 2017. p. 1683-1686.
Rubin, V. L. e. a. (2016). Fake news or truth? using satirical cues to detect potentially misleading news. In Proceedings of the second workshop on computational approaches to deception detection. 2016. p. 7-17.
Silva, R. M. e. a. (2020). Towards automatically filtering fake news in portuguese. In Expert Systems with Applications, v. 146, p. 113199, 2020.
Snscrape (2023). Snscrape: A social networking service scraper in python. GitHub.
Torabi Asr, Fatemeh; Taboada, M. (2019). Big data and quality data for fake news and misinformation detection. In Big Data Society, v. 6, n. 1, p. 2053951719843310, 2019.
US (2014). Joint intelligence: Joint publication 2-0. Createspace Independent Pub.
Vosoughi, Soroush; Roy, D. A. S. (2018). The spread of true and false news online. In Science, v. 359, n. 6380, p. 1146-1151, 2018.
Weigang, L., Enamoto, L. M., Li, D. L., and Rocha Filho, G. P. (2022). New directions for artificial intelligence: human, machine, biological, and quantum intelligence. Frontiers of Information Technology & Electronic Engineering, 23(6):984-990.
Publicado
22/05/2023
Como Citar
GODINHO, Alexandre C.; NUNES, Cristiano M.; MENEGUETTE, Rodolfo I.; GONÇALVES, Vinicius P.; OLIVEIRA, Marlos André M. S. de; OLIVEIRA, José C. M.; ROCHA FILHO, Geraldo P..
STALLA: Um Framework para Análise de Fontes Abertas durante a Pandemia do Covid-19. In: WORKSHOP DE COMPUTAÇÃO URBANA (COURB), 7. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 54-67.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2023.731.
