Intervention algorithm for malicious information in online social networks based on trusted regulator

Abstract

A lot of malicious information such as rumours are hidden in the massive data flow in social online networks. Once this type of malicious information spreads, it could affect social stability in severe cases. This paper introduces the concept of trusted regulator to select key nodes, and we propose a method to hinder the rapid spread of malicious information by blocking accounts and publishing clarification, so that the external disturbances from the chosen nodes and the malicious information could fight against each other. Local control strategy is applied to the propagation of malicious information. Specifically, we first introduce the SIMRT model and an importance indicator based on edge weight, then we propose the reverse intervention algorithm based on the importance indicator. Experiment results on different data sets show that the proposed algorithm can effectively suppress the spread of malicious information.