SAIRIgRg: A rumor-debunking propagation model considering credibility factor in large-scale rumor spreading

In this study, we collected and processed Weibo posts from official debunking accounts and found that user-following relationships can influence the dynamics of message propagation. Building upon the Susceptible, Aware, Infected, Recovered (SAIR) model with memory decay and user reinforcement, we develop the SAIRIgRg model to address the practical scenario of government debunking in the context of large-scale rumor spreading, considering the credibility factor. Through simulations on a real-world Twitter-directed network dataset, where different nodes with varying in-degree and out-degree sizes were chosen as debunking nodes, we analyzed the spread of rumors and debunking information. We discovered that there is little correlation between the initial in-degree and out-degree sizes of nodes and the effectiveness of debunking dissemination. Nodes with smaller average path lengths may not effectively suppress rumors through debunking efforts. Conversely, when debunking is conducted on nodes with larger average path lengths, the higher credibility of debunking messages leads to stronger suppression of rumors and shorter lifespans for the rumors. Additionally, this study conducted a comparison between early and late official debunking. It was discovered that when the spreading of rumors reaches a certain size and intensity, even though early debunking may not influence the lifespan of the rumors, it can greatly reduce the number of users affected by the rumors.