Reverse Intervention for Dealing with Malicious Information in Online Social Networks

Abstract

Malicious information is often hidden in the massive data flow of online social networks. In “We Media'' era, if the system is closed without intervention, malicious information may spread to the entire network quickly, which would cause severe economic and political losses. This paper adopts a reverse intervention strategy from the perspective of topology control, so that the spread of malicious information could be suppressed at a minimum cost. Noting that as the information spreads, social networks often present a community structure and multiple malicious information promoters may appear. Therefore, this paper adopts a divide and conquer strategy and proposes an intervention algorithm based on subgraph partitioning, in which we search for some influential nodes to block or release clarification. The main algorithm consists of two main phases. Firstly, a subgraph partitioning method based on community structure is given to quickly extract the community structure of the information dissemination network. Secondly, a node blocking and clarification publishing algorithm based on the Jordan Center is proposed in the obtained subgraphs. Experiments show that the proposed algorithm can effectively suppress the spread of malicious information with a low time complexity compared with the benchmark algorithms.