A Survey of Approaches to Early Rumor Detection on Microblogging Platforms: Computational and Socio‐Psychological Insights

Abstract

Social media, particularly microblogging platforms, are essential for rapid information sharing and public discussion but often allow rumors, that is, unverified information, to spread rapidly during events or persist over time. These platforms also offer opportunities to study the dynamics of rumors and develop computational methods to assess their veracity. In this paper, we provide a comprehensive review of existing theoretical foundations, interdisciplinary challenges, and emerging advancements in rumor detection research, with a focus on integrating theoretical and computational approaches. Drawing on insights from computer science, cognitive psychology, and sociology, we explore methodologies, such as multimodal fusion, graph‐based models, and attention mechanisms, while highlighting gaps in real‐world scalability, ethical transparency, and cross‐platform adaptability. Using a systematic literature review and bibliometric analysis, we identify trends, methods, and gaps in current research. Our findings emphasize interdisciplinary collaboration to develop adaptable, efficient, and ethical rumor detection strategies. We also highlight the critical role of combining socio‐psychological insights with advanced computational techniques to address the human factors in rumor spread. Furthermore, we emphasize the importance of designing systems that remain effective across diverse cultural and linguistic contexts, enhancing their global applicability. We propose a conceptual framework integrating diverse theories and computational techniques, offering a roadmap for improving detection systems and addressing misinformation challenges on microblogging platforms.