Deciding among fake, satirical, objective and legitimate news: A multi-label classification system

Currently, the widespread of fake news has raised on the political class and society members in general, increasing concerns about the potential of misinformation that can be propagated, appearing on the center of the debate about election results around the world. On the other hand, satirical news has an entertaining purpose and are mistakenly put on the same boat of objective fake news. In this work, we address the differences between objectivity and legitimacy of news documents, treating each article as having two conceptual classes: objective/satirical and legitimate/fake. Thus, we propose a Decision Support System (DSS) based on a text mining pipeline and a set of novel textual features that uses multi-label methods for classifying news articles on those two domains. For validating the approach, a set of multi-label methods was evaluated with a combination of different base classifiers and then compared to a multi-class approach. Results reported our DSS as proper (0.80 F1-score) in addressing the scenario of misleading news from challenging perspective of multi-label modeling, outperforming the multi-class methods (0.71 F1-score) over a real-life news dataset collected from several portals of news.