Avaliação da integração de abordagens de verificação de fake news

Abstract

The agility with which information can be produced, disseminated and consumed by virtue of the internet not only democratized the access to information, but also generated an abundance of it, having as a side effect the facilitation of the propagation of false or biased information, popularly known as Fake News. Due to the high volume and ephemerality of the data, automated methods with artificial intelligence techniques become essential in the Fake News verification process. From the reading of the state of the art, it is observed that the existing approaches have limits of applicability in specific contexts and that there is no approach capable of dealing with different contexts without compromising their results. Based on this, this work proposes the integration of two methods to evaluate the integration of different methods of verification of Fake News in order to expand the scope of application in different contexts. The text classification method obtained an accuracy of 95.33% using Random Forest, while the fact-checking method with question answering was able to adequately answer the elaborated questions. A comparison methodology was proposed to qualitatively analyze the results of the experiments, which allowed the identification of contributions and future work. The texts classified as false negatives in the classification experiment served as a subsidy for the elaboration of the questions tested in the fact-checking experiment with question answering, validating the complementarity between the methods.