Credibility Analysis in Social Big Data

TL;DR

This paper reviews credibility analysis in social big data, discussing approaches to evaluate user trustworthiness and presenting a case study using machine learning to predict trustworthy users across domains.

Contribution

It provides a comprehensive overview of credibility concepts in social big data and demonstrates a machine learning-based method for trustworthiness prediction.

Findings

Machine learning techniques effectively predict trustworthy users.

Semantic analysis enhances trustworthiness evaluation.

Approaches are scalable for large social datasets.

Abstract

The concept of social trust has attracted an attention of information processors/data scientists and information consumers / business firms. One of the main reasons for acquiring the value of SBD is to provide frameworks and methodologies using which the credibility of online social services users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring credibility of social big data. Given the exposed environment's settings and fewer limitations related to online social services, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. This chapter presents an overview of the notion of credibility in the context of SBD. It also list an array of approaches to measure and evaluate the trustworthiness of users and their contents. Finally, a case study is presented that incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.