Measuring trustworthiness of information diffusion by risk discovery process in social networking services

Most of current social network services are vulnerable to malicious actions. For example, rumor (e.g., contaminated and distorted information) can be diffused along the social links. In this paper, given a social network service, we design a peer-to-peer (P2P) network, and propose a robust information diffusion model to efficiently detect the malicious peers from which a risk (i.e., rumor) has been generated on the P2P network. Thereby, by aggregating social interactions among users, a set of interaction sequences are obtained. Given a set of interaction sequences, statistical sequence mining method is exploited to discover a certain social position which provides peculiar patterns on the P2P networks. For evaluating the proposed method, we conducted two experimentations with NetLogo simulation platform for risk discovery on social network.