  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.
