Unveiling Deception: Machine Learning Strategies for Fake News Detection and Classification

In this study, a system is developed to detect and classify fake news whose proliferation significantly threatens information integrity, public trust, and societal stability. This study presents a comprehensive list of ML strategies for fake news detection and classification. It investigates the efficiency of different ML algorithms such as Logistic regression, Decision tree, and Random Forest, in distinguishing between factual and deceptive content. The findings reveal that some algorithms demonstrate high accuracy and reliability in identifying fake news articles. Moreover, a novel approach is proposed for the manual testing of news articles thus enabling real-time verification of information authenticity.