Fake News Detection Using Machine Learning And Natural LanguageProcessing

The rapid spread of misinformation through digital media platforms has emerged asone of the most critical challenges of the modern information age. This paper presents a web-basedFake News Detection System that leverages Natural Language Processing (NLP) and MachineLearning (ML) techniques to classify news headlines and article text as real or fake in real time. Thesystem employs Term Frequency-Inverse Document Frequency (TF-IDF) vectorization combinedwith Logistic Regression classification, achieving reliable detection accuracy without requiringexternal API keys or internet connectivity during inference. The application is deployed as a Flaskweb application, enabling seamless interaction through a browser-based user interface.Experimental results demonstrate that the system effectively identifies linguistic patterns associatedwith misinformation, providing users with confidence scores and interpretable indicators. Thisresearch contributes a practical, lightweight, and easily deployable solution to the growing problemof online fake news detection.