Abstract
Combating the circulation of disinformation has been increasing since this type of news can influence people’s behavior and opinion. One way to deal with this problem is to develop automated fact-checking systems with machine learning techniques. An essential step in the generation of classifying models is data preprocessing. This work compared analysis using different preprocessing methodologies to normalization, transformation, and feature selection in a Portuguese Language Corpus that contained fake and legitimate news. We obtained better results compared to the current approaches presented in the literature.
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Marinho, M., Bastos-Filho, C.J.A., Lins, A. (2021). Evaluating Preprocessing Techniques in Identifying Fake News. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_46
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