Research on dynamic modeling and control mechanisms of rumor spread considering high-order interactions and counter-rumor groups

Complex network theory has been widely applied to rumor propagation modeling and optimizing strategies. Nonetheless, most previous studies have been limited to binary interaction-based network topology, failing to differentiate the impact of various information types. This study proposes a dynamic rumor propagation model based on random simple complexes, integrating counter-rumor groups and higher-order interactions among heterogeneous groups. The proposed model establishes a finer-grained classification framework of rumor-exposed populations and information types (original posts and reposts), leveraging Markov Chain Monte Carlo sampling track the time-evolution of heterogeneous group proportions within empirically calibrated parameter ranges. Moreover, the study elucidates the higher-order coupling dynamics among multiple factors, including topological network metrics, debunking response latency, and collective suppression thresholds, with validation via the Weibo dataset. Results reveal that higher-order interactions accelerate rumor propagation speed; topological complexity and the debunking response rate have a dominant impact on the magnitude of rumor spread; whereas the number of initial counter-rumor nodes and global propagation scale constitute secondary contributing factors. In contrast to forwarding fact-checking information, the study reveals that focusing limited official resources on the production of original fact-checking content is more effective in inhibiting the spread of rumors. The finding is consistent with previous empirical studies. This study deepens comprehension of high-order interactions and behavior coupling in rumor and counter-rumor dissemination, providing practical methodological guidance for controlling irrational rumor dissemination.