A graded decision framework for rumor containment: Modeling the coupled dynamics of credit regulation and rumor propagation in multiplex networks

The governance of online rumors faces growing complexity due to the coupled diffusion of credit risks, where the structural imbalance between credit punishment and credit repair mechanisms constitutes a critical bottleneck restricting governance efficacy. To address this issue, we construct a two-layer coupled heterogeneous network model integrating “credit regulation” and “rumor propagation”. By employing the Micro-Markov Chain Approach (MMCA) and Monte Carlo simulations (MC), we reveal the inter-layer coupling mechanisms and systematically quantifies the synergistic suppression effects of various combinations of credit tools on rumor propagation. Our findings demonstrate that the credit punishment mechanism acts as a primary suppressor, imposing a hard constraint on the rumor scale by raising the propagation threshold. Conversely, the credit repair mechanism serves as a necessary buffer to maintain system resilience, although an excessively rapid repair rate may exacerbate the malicious rebound of rumors. Furthermore, we verify that a tiered punishment strategy-characterized by severe crackdown, moderate stabilization, and lenient tolerance-combined with a gradient repair strategy, achieves optimal suppression effects with minimal regulatory resource input across diverse risk environments. This study provides a quantitative perspective for the refinement of legislation and evidence-based policymaking in the credit governance of online rumors.