A Linguistic-Based Method that Combines Polarity, Emotion and Grammatical Characteristics to Detect Fake News in Portuguese

Abstract

In the last decades, the dissemination of News through digital media has increased the information accessibility previously offered by traditional channels. Despite their benefits, digital media have exacerbated an old problem: the spread of Fake News, (i.e., false News intentionally published). Faced with this scenario, the linguistic approaches to automatic Fake News detection use information that can be directly extracted from the News' text. Several methods based on these approaches use grammatical classification and sentiment analysis over News writing in Portuguese. However, as far as it was possible to observe in the related literature, these methods are limited to the identification of polarity of sentiment (i.e., positive, neutral or negative) existing in the text. Although polarity classification be an effective method for a wide range of natural language processing applications, it does not address language nuances (e.g., emotions such as anger, sadness, etc.) that can provide evidence that a text contains false information. Hence, this study proposes an extended method that, in addition to the grammatical classification and polarity based sentiment analysis, also uses the analysis of emotions to detect Fake News written in Portuguese. The extended method showed promising results in experimental data, obtaining accuracy greater than 92%. In average, the proposed method overcame polarity and gramatical classification based methods in 1.4 percentage points.