CredSaT: Credibility Ranking of Users in Big Social Data incorporating Semantic Analysis and Temporal Factor

TL;DR

CredSaT is a framework that assesses user credibility in big social data by combining semantic analysis and temporal factors, effectively identifying trustworthy users and detecting spammers.

Contribution

It introduces a novel credibility metric that integrates semantic, temporal, and feature-based analysis for fine-grained user ranking in social data.

Findings

Effective in ranking trustworthy users across domains

Capable of detecting spammers and anomalous users

Validated on real-world datasets

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.