Fake news detection using Deep Learning

TL;DR

This paper explores the application of deep learning models, including BERT, to detect fake news from text, aiming to address societal concerns about misinformation's impact on democracy.

Contribution

It proposes three neural network architectures, including one based on BERT, to improve fake news detection using only textual data.

Findings

BERT-based model achieves state-of-the-art results.

Deep learning models effectively discriminate fake news.

The study demonstrates feasibility of text-only fake news detection.

Abstract

The evolution of the information and communication technologies has dramatically increased the number of people with access to the Internet, which has changed the way the information is consumed. As a consequence of the above, fake news have become one of the major concerns because its potential to destabilize governments, which makes them a potential danger to modern society. An example of this can be found in the US. electoral campaign, where the term "fake news" gained great notoriety due to the influence of the hoaxes in the final result of these. In this work the feasibility of applying deep learning techniques to discriminate fake news on the Internet using only their text is studied. In order to accomplish that, three different neural network architectures are proposed, one of them based on BERT, a modern language model created by Google which achieves state-of-the-art results.