An Efficient Supervised Method for Fake News Detection using Machine and Deep Learning Classifiers

Abstract

This paper comes up with the applications of Machine learning and deep learning algorithms for police work the 'fake news', that is, dishonorable news stories that come from the unauthorized article writers. This approach was enforced as software and tested against an information set. Aim is to separate the faux news, among the news spread in the articles. It’s required to create a model which is able to differentiate between “Real” news and “Fake” news. The model was created exploitation numerous deep and machine learning strategies. LSTM technique outperforms different classifiers and achieves the accuracy of 94%.