Rumor propagation dynamic analysis model based on hypergraph integration in public health events.

During public health emergencies, the spread of online rumors can easily trigger public panic and anxiety, exacerbating their negative social impact. How to precisely regulate the dissemination of rumors in cyberspace under such circumstances, thereby enhancing the efficiency of public opinion management, has become a critical issue urgently needing resolution by both administrators and researchers. The study focuses on accurately describing the propagation patterns of rumor information from its source. Therefore, the study constructs a novel H-SNIR (Hypernetwork-Susceptible-Neglected-Infectious-Recovered) model in a hypernetwork. The model comprehensively considers the psychological differences among individuals during rumor propagation, categorizing rumor spreaders into two types: ordinary spreaders and hesitant spreaders. The H-SNIR model introduces a node for neglected spreaders, making the constructed model more consistent with the real-world rumor propagation process. Additionally, by integrating three key dimensions-user influence, topic popularity, and user interaction-the study thoroughly analyzes how these variables affect the rumor-forwarding mechanism in social networks.