Influence Level Assessment in Suppressing the Spread of Undesirable Information in Online Social Networks

Undesirable information can spread rapidly in social networks and cause great damage. This paper introduces an innovative Priority Recovery Control (PRC-SIRS) model aimed at curbing the dissemination of undesirable information. In this model, infected nodes are not left to heal spontaneously. Instead, network administrators strategically prioritize the blocking of infected nodes with the greatest influence. We then propose an innovative Influence Level Assessment (ILA) strategy that fully considers both the likelihood of nodes being influenced and their own influence during information dissemination. Experiments based on real-world social network datasets show that the ILA strategy proposed in this paper outperforms traditional heuristic algorithms.