Fake News Detection: Mining Social Media Data to Detect and Classify Misinformation

Abstract

The proliferation of misinformation on social media platforms poses significant challenges to information integrity and democratic discourse. This paper presents a comprehensive analysis of computational approaches for fake news detection, examining current methodologies that leverage natural language processing, machine learning, and network analysis to identify and classify misinformation. Through a systematic review of empirical studies published between 2017 and 2024, we identify key features and techniques used in fake news detection systems, evaluate their effectiveness, and discuss limitations and future research directions. Our findings reveal that ensemble methods combining linguistic, network, and temporal features achieve accuracy rates of 85-95%, though challenges remain in cross-domain generalization and detecting sophisticated deepfakes. We propose a unified framework for understanding fake news detection methodologies and provide recommendations for developing more robust and scalable systems.