Emotional Fingerprints: A Framework for Identifying Fake News

Abstract

Social media has become one of the main channels of information for human beings due to the immediacy and social interactivity they offer, allowing, in some cases, the publication of whatever each user considers relevant. This has led to the generation of false news, or fake news, publications that only seek to generate uncertainty, misinformation, or bias readers' opinions. It has been shown that humans are not able to fully identify whether an article is factual or fake news. Because of this, models have emerged that seek to characterize and identify articles based on data mining and machine learning. This article proposes a three-layer framework, the main objective of which is to characterize the emotions present in fake news and to serve as a tool for associating the emotional state and the most likely intention of the person publishing a fake news story.