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Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks

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  • Yingxu Wang

    (International Institute of Cognitive Informatics and Cognitive Computing (ICIC), University of Calgary, Calgary, Alberta, Canada)

  • Victor J. Wiebe

    (International Institute of Cognitive Informatics and Cognitive Computing (ICIC), University of Calgary, Calgary, Alberta, Canada)

Abstract

Big data are products of human collective intelligence that are exponentially increasing in all facets of quantity, complexity, semantics, distribution, and processing costs in computer science, cognitive informatics, web-based computing, cloud computing, and computational intelligence. This paper presents fundamental big data analysis and mining technologies in the domain of social networks as a typical paradigm of big data engineering. A key principle of computational sociology known as the characteristic opinion equilibrium is revealed in social networks and electoral systems. A set of numerical and fuzzy models for collective opinion analyses is formally presented. Fuzzy data mining methodologies are rigorously described for collective opinion elicitation and benchmarking in order to enhance the conventional counting and statistical methodologies for big data analytics.

Suggested Citation

  • Yingxu Wang & Victor J. Wiebe, 2014. "Big Data Analytics on the Characteristic Equilibrium of Collective Opinions in Social Networks," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 8(3), pages 29-44, July.
    • Handle: RePEc:igg:jcini0:v:8:y:2014:i:3:p:29-44
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    Cited by:

    1. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    2. Bayrak, Tuncay, 2021. "A framework for decision makers to design a business analytics platform for distributed organizations," Technology in Society, Elsevier, vol. 67(C).

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