Fake News Detection: a comparison between available Deep Learning techniques in vector space

TL;DR

This paper compares various deep learning techniques for fake news detection by representing news in vector spaces and analyzing their effectiveness through extensive experiments.

Contribution

It provides a comparative analysis of deep learning methods for fake news detection using vector space representations, highlighting their relative performances.

Findings

Certain deep learning models outperform others in accuracy.

Vector space representations significantly impact detection effectiveness.

Experimental results reveal key factors influencing model performance.

Abstract

Fake News Detection is an essential problem in the field of Natural Language Processing. The benefits of an effective solution in this area are manifold for the goodwill of society. On a surface level, it broadly matches with the general problem of text classification. Researchers have proposed various approaches to tackle fake news using simple as well as some complex techniques. In this paper, we try to make a comparison between the present Deep Learning techniques by representing the news instances in some vector space using a combination of common mathematical operations with available vector space representations. We do a number of experiments using various combinations and permutations. Finally, we conclude with a sound analysis of the results and evaluate the reasons for such results.