Identification of “Fake News” in Brazilian political context: a computational approach

• Laura D. de Almeida UAM http://orcid.org/0000-0003-4399-495X
• Victor Fuzaro UAM https://orcid.org/0000-0002-2409-9928
• Falmer V. Nieto UAM https://orcid.org/0000-0002-0039-2265
• André L. M. Santana UAM / USP https://orcid.org/0000-0002-9807-3253

DOI: https://doi.org/10.5753/wics.2021.15966

Abstract

This paper shows a computational solution's main results to analyze Brazilian fake news in a political context, and investigate which Machine Learning Algorithm, between Support Vector Machine and Naive Bayes, reach the best result to classify, in a natural language context, whether Brazilian political news is fake or not. The better performance was reached by combining SVM (RBF) + BOW with 80,4% accuracy, 82% precision, 76% recall, 78% of F1-Score, and 88% of AUC. The non-probabilistic algorithms proved to be better in the classification of fake news, thus, the results allow to present a path for future works.

Keywords: Fake News, Machine Learning, Natural Language Processing

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