FAKE NEWS AND MISINFORMATION: A SYSTEMATIC REVIEW OF DETECTION AND IMPACT STUDIES

Abstract

The proliferation of fake news and misinformation on digital platforms has become one of the most pressing challenges of the digital age, significantly impacting public discourse, democratic processes, and societal trust. This systematic literature review (SLR) examines the current state of research on fake news detection and its societal impacts, analyzing studies published between 2020 and 2024. Following PRISMA guidelines, this review synthesizes findings from computational detection methods, psychological and social impact studies, and intervention strategies. The analysis reveals three dominant research themes: (1) Computational Detection and Machine Learning Approaches, (2) Psychological and Social Impact Studies, and (3) Platform-based and Intervention Strategies. Our findings indicate that while significant advances have been made in automated detection using deep learning and natural language processing techniques, challenges remain in cross-domain generalization, multimodal content analysis, and addressing the psychological factors that drive misinformation consumption and sharing. This review contributes to the understanding of fake news as a multifaceted phenomenon requiring interdisciplinary approaches for effective mitigation.