A Review on Fake News Detection using Machine Learning

Abstract

Fake news, which is defined as material that has been shared with the intention of defrauding people, has been growing quickly and widely recently. This kind of misinformation is dangerous to social cohesion and wellbeing because it exacerbates political polarisation and public mistrust of authority figures. As a result, false news is an issue that has a big impact on our social lives, especially in politics. In order to address this issue, this study suggests brand-new methods based on machine learning (ML) and deep learning (DL) for the fake news identification system. This survey deals with a review of existing machine learning algorithms Naïve Bayes, Convolutional Neural Network, LSTM, Neural Network, Support Vector Machine proposed for detecting and reducing fake news from different social media platforms like Facebook, whatsapp, twitter, etc. This review provides a comprehensive detail including data mining perspective, evaluation metrics, and representative datasheets.