A Machine Learning Framework for Countering Fake News Propagation

Abstract - The widespread use of social media has accelerated the spread of fake news, eroding public trust and making fact verification difficult. This project presents a machine learning framework using Natural Language Processing (NLP) and Decision Tree algorithms to classify news articles as real or fake. The system includes data preprocessing, TF-IDF-based feature extraction, and model training to detect misinformation accurately. Designed for both accuracy and interpretability, the framework offers a scalable solution for real-time fake news detection, with strong potential for integration into social media platforms and automated fact-checking tools. Keywords : Fake News Detection, Machine Learning Natural Language Processing (NLP),Decision Tree Classifier , TF-IDF , Text Classification , Misinformation,Social Media Analysis, Data Preprocessing, Feature Extraction