Fake News Detection Using Machine learning Technique : A Review

Abstract

The proliferation of fake news has become a significant challenge in the digital era, threatening the credibility of information shared online. To combat this menace, researchers have turned to machine learning techniques for automated detection. This paper presents a comprehensive review of various machine learning approaches employed for fake news detection. We analyze a wide range of methodologies, including supervised, unsupervised, and deep learning algorithms, discussing their strengths and limitations. Moreover, we examine the datasets and evaluation metrics commonly used in this domain. By synthesizing existing research, we identify key trends and promising directions for future investigations. The review aims to provide a comprehensive understanding of the state-of-the-art in fake news detection using machine learning, fostering advancements in this critical field of research.