Search

The innovative existence of social network has revolutionized the way we communicate, perceive, and interact with others. Internet has become a medium that connects our lives together and attracts many people to spend time with it, and to engage in different activities from casual to business. However, not everyone is trustworthy. One of the main issues in online social networks is the information overload. Some user accounts may be created with hidden agenda and not represent actual people. Our aim in this paper is to develop and test a method for evaluate the credibility of online users based on their interactions with others. We propose an analysis method that uses several analytic measurements for ranking users in Social Networks based on the reputation models such as PageRank, SocialRank, TwitterRank and MetricRank. We also utilize content classification to improve the ranking effectiveness and integrate them to evaluate the consistency of interaction dialogues.

The necessary operation in every sector of organization is finding a talented or experienced individual to participate in a team that will handle specific tasks. Having the right people in the team would increase the team performance and allow the team to be more effective in delivery the result. Research and development play important roles in the development of science and technology. Peer-reviewed publication is one of the main methods used for evaluating research. Normally, editor of academic journal will find researchers for reviewing research based on mainly the research network. However, there are various and interdisciplinary types of thesis nowadays. They made more difficult results for finding reviewers. This paper aims to develop the system for automatically suggest relevant experts given the task of reviewing the scientific paper. This inevitably would assure the quality of the research process and provide an effective way to manage the publication cycle. The system will automatically find related reviewer for reviewing papers based on the co-author networks, reference network and the reputation model.