An Approach for Time-aware Domain-based Analysis of Users Trustworthiness in Big Social Data
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In Online Social Networks (OSNs) there is a need for better understanding of social trust in order to improve the analysis process and mining credibility of social media data. Given the open environment and fewer restrictions associated with OSNs, the medium allows legitimate users as well as spammers to publish their content. Hence, it is essential to measure users’ credibility in various domains and accordingly define influential users in a particular domain(s). Most of the existing approaches of trustworthiness evaluation of users in OSNs are generic-based approaches. There is a lack of domain-based trustworthiness evaluation mechanisms. In OSNs, discovering users’ influence in a specific domain has been motivated by its significance in a broad range of applications such as personalized recommendation systems and expertise retrieval. The aim of this paper is to present an approach to analysing domain-based user’s trustworthiness in OSNs. We provide a novel distinguishing measurement for users in a set of knowledge domains. Domains are extracted from the user’s content using semantic analysis. In order to obtain the level of trustworthiness, a metric incorporating a number of attributes extracted from content analysis and user analysis is consolidated and formulated by considering temporal factor. We show the accuracy of the proposed algorithm by providing a fine-grained trustworthiness analysis of users and their domains of interest in the OSNs using big data Infrastructure.
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