The document discusses using machine learning techniques for fake news detection on social media platforms. It proposes using distributed learning across a cluster to extract features from news articles, including user-based, content-based, and social context-based features. Recurrent neural networks are used to model news articles based on title and body content to classify real and fake news. Evaluation metrics show the model achieves 92.45% F1 score for detection, outperforming existing models.