Reputation management in online social networks a new clustering-based approach

Trust and reputation management stands as a corner stone within the Online Social Networks (OSNs) since they ensure a healthy collaboration relationship among participants. Currently, most trust and reputation systems focus on evaluating the credibility of the users. The reputation systems in OSNs have as objective to help users to make difference between trustworthy and untrustworthy, and encourage honest users by rewarding them with high trust values. Computing reputation of one user within a network requires knowledge of trust degrees between the users. In this paper, we propose a new Clustering Reputation algorithm, called RepC, based on trusted network. This algorithm classifies the users of OSNs by their trust similarity such that most trustworthy users belong to the same cluster. We conduct extensive experiments on a real online social network dataset from Twitter. Experimental results show that our algorithm generates better results than do the pioneering approaches of the literature.