Machine Learning Techniques for Identifying Fake News: An Overview

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. To combat this issue, this study proposes innovative approaches grounded in machine learning (ML) and deep learning (DL) for enhancing fake news detection systems. This investigation encompasses a thorough examination of established machine learning algorithms such as Naïve Bayes, Convolutional Neural Networks, Long Short-Term Memory networks, Neural Networks, and Support Vector Machines. These algorithms are explored in the context of identifying and mitigating fake news across various social media platforms, including Facebook, WhatsApp, Twitter, and more. This review offers a comprehensive overview that includes perspectives from data mining, evaluation metrics, and representative datasets, contributing to a deeper understanding of the strategies employed to combat the proliferation of fake news