Generation of Fake content using Machine Learning RNN Technique

Abstract

A wider audience is being exposed to fake news than ever before. The proliferation of social networking sites and direct messaging systems is the primary culprit. The problem at hand is developing an algorithm using deep learning that can discern between real and fake news articles. In order to accomplish this, this research first examines a few datasets that allow for the coexistence of fake and true news. Then we review some of the existing research on deep learning algortihms and algorithms which are applied in fake news classification. This paper focuses on implementing an RNN algorithm with the minimum data preprocessing possible and achieving maximum accuracy.