Big Data ML-Based Fake News Detection Using Distributed Learning

Users rely heavily on social media to consume and share news, facilitating the mass dis-semination of genuine fake stories. The proliferation misinformation various platforms has serious consequences for society. inability differentiate between sev-eral forms false news Twitter is a major obstacle effective detection news. Researchers have made progress toward solution by placing greater emphasis methods identifying bogus dataset FNC-1, which includes four categories will be used in this study. state-of-the-art spotting are evaluated compared using big data technology (Spark) machine learning. methodology study employed decentralized Spark cluster create stacked ensemble model. Following feature extraction N-grams, Hashing TF-IDF, count vectorizer, we proposed classification results show that suggested model superior performance 92.45% F1 score 83.10 % baseline approach. achieved an additional 9.35% techniques.