The Evolution and Frontiers of Rumor Propagation Research: From Propagation Dynamics to Intelligent Governance
DOI:
https://doi.org/10.54097/g5hb0695Keywords:
Rumor Propagation, Propagation Dynamics, Immunization Strategies, Real-world Social Networks, Large Language Models (LLMs)Abstract
Rumor spreading is a long-standing problem in the information ecosystem, and it has drawn considerable attention in recent years as social media became widely used and generative artificial intelligence rose. This paper systematically reviews how theories have evolved and what cutting-edge progress has been made in rumor propagation research. At the modeling level, the field has kept deepening: from classic epidemic models to heterogeneous network models, from single-rumor to multi-rumor interactions, and from static analysis to temporal modeling. At the mechanism level, user behavioral factors—such as emotional arousal, novelty perception, and identity-protective cognition—have been gradually incorporated into models, revealing the psychological and social mechanisms that make rumor spreading different from disease transmission. At the real-world network level, empirical studies have found that false information clearly outperforms true information in propagation depth, breadth, and speed, and network topology plays a significant moderating role in propagation thresholds. As for governance strategies, research on immunization strategies has expanded from global targeted immunization to acquaintance immunization, which requires only local information; however, systematic validation on real-world networks is still insufficient. Artificial intelligence technologies are reshaping the paradigms of rumor detection and control, and the integration of large language models (LLMs) and graph neural networks (GNNs) has emerged as the most promising technical pathway. Future research needs to achieve continuous breakthroughs in empirical validation on real-world networks, the integration of propagation-detection-control frameworks, and cross-platform governance.
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