Sentiment Impact on Fake News Detection: A Preliminary Study

The information disorder phenomenon represents one of the main challenges for the current society, that researchers of a huge variety of scientific areas are trying to solve. To date, the majority of the studies carried out in this context are focused on Machine Learning-based Fake News detectors trained on textual data. Despite the numerous attempts available in the literature and the promising results of such models, unfortunately, the expectations are not truly met considering their use in real-world scenarios. The main limitations are directly related to the nature of the phenomenon since the model trained on past events can’t understand and classify novel contents and breaking news. In the current work, we study if sentiment discrepancy between news and its associated evidence can determine the possible presence of fake content. In fact checking activities, the evidence are inferred claims or information used to accept or reject shared news [2]. To do that we have conducted a preliminary study in which the outcome is a metric called Negativity Score. This metric can be used as a feature in Fake News detection models and automated fact-checking activities. For completeness, we have also provided a framework that uses the proposed metric. The results of preliminary experiments highlight the possibility of exploiting sentiment aspects in addition to more common and well-known approaches.