Adaptive Approach for Rumors Influence Minimization in Dynamic Social Networks

Breaking news is a kind of rumor that can reach a large number of users and is characterized by its short-term life. Therefore, it’s highly recommended to seek a solution to limit its spread through online social networks (OSNs). This work proposes an approach to mitigate the influence of breaking news rumors on dynamic social networks. Initially, we introduce a representation of a continuous node-static evolving network. Subsequently, through an analysis of existing literature gaps, we present Adaptive Graph Protection with User Experience (AGPUX). AGPUX is designed to select the optimal set of nodes in each iteration, aiming to minimize the dissemination of malicious information. Systematically, experiments have been conducted to evaluate the performance of the proposed approach on real networks. The results showed that the proposed AGPUX approach could outperform the most recent methods in the literature.