Inteligência Artificial Aplicada a Detecção de Fake News
Resumo
Informações veiculadas pela mídia nem sempre se apresentam com conteúdo confiável. As Fake News são notícias que não representam a realidade, mas que são compartilhadas como se fossem verídicas. O uso mal intencionado dessas informações pode comprometer a sociedade, manipulando a opinião do leitor. Este trabalho se propõe a fazer a detecçã automática de Fake News por meio de técnicas de Inteligência Artificial, utilizando algoritmos comoÁrvore de Decisão Naive Bayes, SVM e Deep Learning. Utiliza-se duas abordagens, obtendo uma acurácia de 92,36% no melhor caso.
Palavras-chave:
Fake news, inteligência artificial, árvore de decisão
Referências
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Granik, M. and Mesyura, V. (2017). Fake news detection using naive Bayes classifier.
Monteiro, R. A., Santos, R. L. S., Pardo, T. A. S., de Almeida, T. A., Ruiz, E. E. S., and Vale, O. A. (2018). Contributions to the Study of Fake News in Portuguese: New Corpus and Automatic Detection Results. Springer International Publishing.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher,
M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
P´erez-Rosas, V. and Mihalcea, R. (2015). Experiments in open domain deception detection. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1120–1125.
Rubin, V., Conroy, N., Chen, Y., and Cornwell, S. (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, pages 7–17.
Samarth Agrawal, Towards Data Science (2019). Sentiment Analysis using LSTM (Step-by-Step Tutorial) - Using PyTorch framework for Deep Learning.
Tandoc Jr, E. C., Lim, Z. W., and Ling, R. (2018). Defining “fake news” a typology of scholarly definitions. Digital journalism, 6(2):137–153.
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Appling, D. S., Briscoe, E. J., and Hutto, C. J. (2015). Discriminative models for predicting deception strategies. In Proceedings of the 24th International Conference on World Wide Web, pages 947–952. ACM.
Aur´elio, B. (1986). Novo dicion´ario da l´ýngua portuguesa. Nova Fronteira.
Bird, S., Klein, E., and Loper, E. (2009). Natural language processing with Python. ”O’Reilly Media, Inc.”.
George, J. and Keane, B. (2006). Deception detection by third party observers. In deception detection symposium, 39th annual Hawaii international conference on system sciences.
Gilda, S. (2017). Evaluating machine learning algorithms for fake news detection. In 2017 IEEE 15th Student Conference on Research and Development (SCOReD), pages 110–115. IEEE.
Gimenes, G., Cordeiro, R. L., and Rodrigues-Jr, J. F. (2017). Orfel: efficient detection of defamation or illegitimate promotion in online recommendation. Information Sciences, 379:274–287.
Granik, M. and Mesyura, V. (2017). Fake news detection using naive Bayes classifier.
Monteiro, R. A., Santos, R. L. S., Pardo, T. A. S., de Almeida, T. A., Ruiz, E. E. S., and Vale, O. A. (2018). Contributions to the Study of Fake News in Portuguese: New Corpus and Automatic Detection Results. Springer International Publishing.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher,
M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
P´erez-Rosas, V. and Mihalcea, R. (2015). Experiments in open domain deception detection. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 1120–1125.
Rubin, V., Conroy, N., Chen, Y., and Cornwell, S. (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, pages 7–17.
Samarth Agrawal, Towards Data Science (2019). Sentiment Analysis using LSTM (Step-by-Step Tutorial) - Using PyTorch framework for Deep Learning.
Tandoc Jr, E. C., Lim, Z. W., and Ling, R. (2018). Defining “fake news” a typology of scholarly definitions. Digital journalism, 6(2):137–153.
TensorFlow: library (2019). TensorFlow.
Wikip´edia (2019). Spamming - Wikipedia, the free encyclopedia.
Publicado
25/09/2019
Como Citar
OLIVEIRA, Leandro Massetti R.; COSTA, Aline Mayara S.; FERNANDES, Vandecia Rejane M..
Inteligência Artificial Aplicada a Detecção de Fake News. In: ESCOLA REGIONAL DE COMPUTAÇÃO DO CEARÁ, MARANHÃO E PIAUÍ (ERCEMAPI), 7. , 2019, São Luís.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
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p. 230-237.
