An Efficient User Trust Computation Using Machine Learning Methods in Online Social Networks

Social networks are good platforms for likeminded people to exchange their views and thoughts. With the rapid growth of web applications, social networks became huge networks with million numbers of users. On the other hand, number of malicious activities by untrustworthy users also increased. Users must estimate the people trustworthiness before sharing their personal information with them. Since the social networks are huge and complex, the estimation of user trust value is not trivial task and could gain main researchers focus. Some of the mathematical methods are proposed to estimate the user trust value, but still they are lack of efficient methods to analyze user activities. In this paper “An Efficient Trust Computation Methods Using Machine Learning in Online Social Networks- TCML” is proposed. Here the twitter user activities are considered to estimate user direct trust value. The trust values of unknown users are computed through the recommendations of common friends. The available twitter data set is unlabeled data, hence unsupervised methods are used in categorization (clusters) of users and in computation of their trust value. In experiment results, silhouette score is used in assessing of cluster quality. The proposed method performance is compared with existing methods like mole and tidal where it could outperform them.