This document discusses a machine learning framework for detecting fake news. It presents the problem statement of identifying fake news articles on social media and developing an automated system using natural language processing and machine learning techniques. It describes using TF-IDF for feature extraction and testing various machine learning algorithms, including logistic regression, decision trees, random forests, and boosting. It compares the accuracy of these models on a dataset of over 47,000 real and fake news posts and finds that random forests achieved the highest accuracy of 99.47%. The conclusion emphasizes the importance of detecting fake news on social media to prevent the spread of misinformation.