Abstract
During the coronavirus pandemic, the problem of misinformation arose once again, quite intensely, through social networks. In many developing countries such as Brazil, one of the primary sources of misinformation is the messaging application WhatsApp. However, due to WhatsApp’s private messaging nature, there still few methods of misinformation detection developed specifically for this platform. Additionally, a MID model built to Twitter or Facebook may have a poor performance when used to classify WhatsApp messages. In this context, the automatic misinformation detection (MID) about COVID-19 in Brazilian Portuguese WhatsApp messages becomes a crucial challenge. In this work, we present the COVID-19.BR, a data set of WhatsApp messages about coronavirus in Brazilian Portuguese, collected from Brazilian public groups and manually labeled. Besides, we evaluated a series of misinformation classifiers combining different techniques. Our best result achieved an F1 score of 0.778, and the analysis of errors indicates that they occur mainly due to the predominance of short texts. When texts with less than 50 words are filtered, the F1 score rises to 0.857.
Supported by CAPES and LSBD.
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Choudrie, J., Banerjee, S., Kotecha, K., Walambe, R., Karende, H., Ameta, J.: Machine learning techniques and older adults processing of online information and misinformation: a covid 19 study. Comput. Hum. Behav. 119, 106716 (2021). https://doi.org/10.1016/j.chb.2021.106716
Elhadad, M.K., Li, K.F., Gebali, F.: Detecting misleading information on COVID-19. IEEE Access 8, 165201–165215 (2020). https://doi.org/10.1109/ACCESS.2020.3022867
Garimella, K., Tyson, G.: WhatsApp, doc? A first look at WhatsApp public group data. arXiv preprint arXiv:1804.01473 (2018)
Giachanou, A., Zhang, G., Rosso, P.: Multimodal multi-image fake news detection. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 647–654 (2020). https://doi.org/10.1109/DSAA49011.2020.00091
Kim, S.B., Han, K.S., Rim, H.C., Myaeng, S.H.: Some effective techniques for naive bayes text classification. IEEE Trans. Knowl. Data Eng. 18(11), 1457–1466 (2006)
Kolluri, N.L., Murthy, D.: CoVerifi: a COVID-19 news verification system. Online Soc. Netw. Media 22, 100123 (2021). https://doi.org/10.1016/j.osnem.2021.100123
Pranckevičius, T., Marcinkevičius, V.: Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Baltic J. Mod. Comput. 5(2), 221 (2017)
Prasetijo, A.B., Isnanto, R.R., Eridani, D., Soetrisno, Y.A.A., Arfan, M., Sofwan, A.: Hoax detection system on Indonesian news sites based on text classification using SVM and SGD. In: 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), pp. 45–49. IEEE (2017)
Rennie, J.D., Shih, L., Teevan, J., Karger, D.R.: Tackling the poor assumptions of naive bayes text classifiers. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp. 616–623 (2003)
Resende, G., Messias, J., Silva, M., Almeida, J., Vasconcelos, M., Benevenuto, F.: A system for monitoring public political groups in WhatsApp. In: Proceedings of the 24th Brazilian Symposium on Multimedia and the Web, WebMedia 2018, pp. 387–390. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3243082.3264662
Su, Q., Wan, M., Liu, X., Huang, C.R.: Motivations, methods and metrics of misinformation detection: an NLP perspective. Nat. Lang. Process. Res. 1, 1–13 (2020). https://doi.org/10.2991/nlpr.d.200522.001
Waterloo, S.F., Baumgartner, S.E., Peter, J., Valkenburg, P.M.: Norms of online expressions of emotion: comparing Facebook, Twitter, Instagram, and WhatsApp. New Media Soc. 20(5), 1813–1831 (2018)
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Forte Martins, A.D., Cabral, L., Chaves Mourão, P.J., Monteiro, J.M., Machado, J. (2021). Detection of Misinformation About COVID-19 in Brazilian Portuguese WhatsApp Messages. In: Métais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds) Natural Language Processing and Information Systems. NLDB 2021. Lecture Notes in Computer Science(), vol 12801. Springer, Cham. https://doi.org/10.1007/978-3-030-80599-9_18
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