Fake News Detection using Machine Learning

Abstract

Abstract: In recent years, the proliferation of fake news has precipitated numerous social and political quandaries. As the predominant source of information shifts towards digital platforms, discerning accountability for disseminated opinions becomes increasingly challenging, hindering authentication of received information. Given the pervasive nature of ecological and societal dilemmas, the role of machine learning in combating fake messages on social media is paramount. The virality of messages, whether genuine or fabricated, underscores the necessity for an automated, resilient, dependable, and efficient detection mechanism amidst multifarious challenges. This review delves into the contemporary landscape of fake news detection mechanisms within social media. By examining the contextual backdrop of fake news and its ramifications on users, we explore various methodological approaches categorized as content-based, social context-based, and hybrid-based methods. Culminating with an elucidation of four pivotal research challenges, this paper aims to steer future endeavors towards advancing the field.