Title: CredSaT: Credibility Ranking of Users in Big Social Data incorporating Semantic Analysis and Temporal Factor,
Abstract: The widespread use of big social data has pointed the research community in
several significant directions. In particular, the notion of social trust has
attracted a great deal of attention from information processors | computer
scientists and information consumers | formal organizations. This is evident in
various applications such as recommendation systems, viral marketing and
expertise retrieval. Hence, it is essential to have frameworks that can
temporally measure users credibility in all domains categorised under big
social data. This paper presents CredSaT (Credibility incorporating Semantic
analysis and Temporal factor): a fine-grained users credibility analysis
framework for big social data. A novel metric that includes both new and
current features, as well as the temporal factor, is harnessed to establish the
credibility ranking of users. Experiments on real-world dataset demonstrate the
effectiveness and applicability of our model to indicate highly domain-based
trustworthy users. Further, CredSaT shows the capacity in capturing spammers
and other anomalous users.