Big Data ML-Based Fake News Detection Using Distributed Learning

Abstract

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