A framework for characterizing Fake News in terms of emotions

Abstract

Social networks have become one of the main channels of information for humans due to the immediacy and social interactivity they offer, allowing in some cases to publish what each user considers pertinent. This has led to the generation of false news or Fake News, publications that only seek to generate uncertainty, misinformation or bias the opinion of readers. It has been shown that humans are not able to fully identify whether an article is really a fact or a Fake News. Due to this, models emerge that seek to characterize and identify articles based on data mining and machine learning. This article proposes a three-layer framework, whose main objective is to characterize the emotions present in Fake News and to be a tool that allows associating the emotional state and the most probable intention of the person who publishes a Fake News story.