Abstract
Fake news are a problem of our time. They may influence a large number of people on a wide range of subjects, from politics to health. Although they have always existed, the volume of fake news has recently increased due to the soaring number of users of social networks and instant messengers. These news may cause direct losses to people and corporations, as fake news may include defamation of people, products and companies. Moreover, the scarcity of labeled datasets, mainly in Portuguese, prevents training classifiers to automatically filter such documents. In this paper, we investigate the issue for the Portuguese language. Inspired by previous initiatives for other languages, we introduce the first reference corpus in this area for Portuguese, composed of aligned true and fake news, which we analyze to uncover some of their linguistic characteristics. Then, using machine learning techniques, we run some automatic detection methods in this corpus, showing that good results may be achieved.
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We could realize that most of the checked sites shared many fake news.
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Half truth may be defined as the case in which some actual facts are told in order to give support to false facts [4].
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We also remove numeric values in order to help avoiding sparsity.
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The authors are grateful to FAPESP, CAPES and CNPq for supporting this work.
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Monteiro, R.A., Santos, R.L.S., Pardo, T.A.S., de Almeida, T.A., Ruiz, E.E.S., Vale, O.A. (2018). Contributions to the Study of Fake News in Portuguese: New Corpus and Automatic Detection Results. In: Villavicencio, A., et al. Computational Processing of the Portuguese Language. PROPOR 2018. Lecture Notes in Computer Science(), vol 11122. Springer, Cham. https://doi.org/10.1007/978-3-319-99722-3_33
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