An empirical comparison of machine learning versus deep learning algorithms for fake news detection in social networks

Abstract

Social networks have become one of the leading information channels for human beings due to the immediacy and social interactivity they offer, allowing, in some cases, to publish what each user considers relevant. This usage has brought with it the generation of false news or Fake News, publications that only seek to generate uncertainty, misinformation, or skew the readers’ opinion. It has been shown that the human being is not able to fully identify whether an article is actually a fact or a Fake News; due to this, models that seek to characterize and identify articles based on data mining and machine learning emerge. This article empirically compares different machine learning and deep learning schemes to identify fake news; data sets extracted from state of the art are used to accomplish this. The results obtained based on the sampling technique used and the Tf-Idf vector representation of the corpus indicate a significant improvement in accuracy in contrast to the results obtained in the state of the art considering the FakeNewsNet repository.