Fake news detection based on explicit and implicit signals of a hybrid crowd: An approach inspired in meta-learning

Abstract The problem of automatic Fake News detection in digital media of news distribution (DMND - e.g., social networks, online newspaper, etc) has become even more relevant. Among the main detection approaches, the one based on crowd signals from DMND users has stood out by obtaining promising results. In essence, in order to classify a piece of news as fake or not fake, such approach explores the collective sense by combining opinions (signals, i.e., votes about the classification of some news) of a high number of users (crowd), considering the reputations of these users regarding their capacity of identifying Fake News. Although promising, the Crowd Signals approach has a significant limitation: it depends on the explicit user opinion (which is not always available) about the classification of the analyzed news. Such unavailability may be caused by the absence of a functionality in the DMND that collects user opinion about the news, or by the simple option of the users in not giving their opinion. Facing this limitation, the present work raises the hypothesis that it is possible to build models of Fake News detection with a performance comparable to the Crowd Signals based approach, avoiding the dependence on the explicit opinion of DMND users. To validate this hypothesis, the present work proposes HCS, an approach based on crowd signals that considers implicit user opinions instead of the explicit ones. The implicit opinions are inferred from the behavior of users concerning the dissemination of the news analyzed. Inspired in Meta-Learning, the HCS can also use the explicit opinions from machines (news classification models) to complement the implicit user opinions by means of hybrid Crowds. Experiments carried out in five datasets presented significant evidence that confirms the raised hypothesis. Even without considering DMND users’ explicit opinions, HCS was able to achieve results comparable to the ones produced by the Crowd Signals approach. Besides that, the results also revealed a performance improvement of HCS when the implicit opinions of the users were combined with the explicit opinions of machines.