A Dynamic Misinformation Influence Minimization Method for Social Networks

Abstract

With the rapid development of social networks, the speed and volume of information spread are increasing. However, the spread of misinformation in social networks may lead to public opinion deviation and have negative influence on the society. With the scale expansion of social networks, it is impossible to have a central controller to detect the misinformation and minimize its influence in social networks. To this end, this paper proposes a decentralized method to handle the misinformation in social networks, which enables users to cooperatively shift the attention of the target user from the misinformation, so as to reduce the influence of misinformation to the society. In our method, an intelligent agent based model is established to simulate users and their relationships in social networks. Then, intelligent agents can explore message sending paths to the target user in a decentralized manner. Finally, suitable messages and their sending paths are dynamically selected to efficiently and effectively shift the attention of the target user. The experiments on real social network datasets (i.e., Twitter, Wiki-Vote, Epinions) indicate the good performance of the proposed method in terms of the attention shift of the target user and minimizing the influence of misinformation on the society.

Keywords

Misinformation influence minimization / decentralized methods / social network / intelligent agents

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