Credibility Analysis in Social Big Data

Bilal Abu-Salih (), Pornpit Wongthongtham (), Dengya Zhu (), Kit Yan Chan () and Amit Rudra ()
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Bilal Abu-Salih: The University of Jordan
Pornpit Wongthongtham: The University of Western Australia
Dengya Zhu: Curtin University
Kit Yan Chan: Curtin University
Amit Rudra: Curtin University

Chapter Chapter 3 in Social Big Data Analytics, 2021, pp 61-88 from Springer

Abstract: Abstract The concept of social trust has attracted the 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 lists 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.

Keywords: Social credibility; Social spam; Social influence; Semantic analysis; Information retrieval (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-33-6652-7_3

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DOI: 10.1007/978-981-33-6652-7_3

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