Fake News Detection Using Machine Learning and Deep Learning

Abstract In the digital age, the rapid spread of misinformation poses a serious threat. This research presents a comprehensive fake news detection system leveraging NLP and deep learning. We examine various techniques (traditional ML vs. deep models), describe a BERT-based classifier, and detail a deployed Flask web application with secure login and analytics. Using benchmark datasets and extensive preprocessing, we achieve high accuracy (95–97%)[1][2]. This fully integrated system combines rigorous data handling, feature extraction, and model evaluation, providing a practical solution to identify fake news in real time. Detailed experimental results, including model comparisons and statistical analyses, are presented. Keywords— Fake News Detection, NLP, BERT, Machine Learning, Flask, TF-IDF, Deep Learning, Ensemble, Dataset Analysis.