Framework para Caracterizar Fake News en Terminos de Emociones

TL;DR

This paper proposes a three-layer framework to characterize emotions in Fake News, aiming to aid in identifying emotional and intentional states of the audience for better detection.

Contribution

It introduces a novel three-layer framework specifically designed to analyze emotional content in Fake News articles.

Findings

Framework effectively characterizes emotions in Fake News

Potential to improve Fake News detection methods

Lays groundwork for analyzing audience emotional responses

Abstract

Social networks have become one of the main information channels for human beings due to the immediate and social interactivity they offer, allowing in some cases to publish what each user considers relevant. This has brought with it the generation of false news or Fake News, publications that only seek to generate uncertainty, misinformation or skew the opinion of readers. It has been shown that the human being is not capable of fully identifying whether an article is really a fact or a Fake News, due to this it is that models arise 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 be a tool for future work that identifies the emotional state and intentional state of the public.