A Web-Based Application for Fake News Detection in Twitter with Machine Learning

The proliferation of social media platforms has dramatically accelerated the spread of both genuine and false information. Twitter, in particular, has emerged as a primary source of breaking news, but its open nature has made it equally prone to the circulation of misleading or fabricated stories. This research presents a web-based application for fake news detection using Natural Language Processing (NLP), Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction, and the Passive Aggressive Classifier (PAC) for classification. The system processes textual inputs—either from the user or random samples—cleans and tokenizes the text, converts it into numerical vectors, and predicts whether the content is FAKE or REAL. The model was evaluated on two datasets: the Kaggle Fake News Dataset and the Simon Fraser University Fake News Dataset, achieving an accuracy between 92% and 97%. Developed using the Flask web framework, the application offers an intuitive interface for real-time fake news detection.