Social Set Analysis: A Set Theoretical Approach to Big Data Analytics

    Research output: Contribution to journalJournal articleResearchpeer-review

    Abstract

    Current analytical approaches in computational social science can be characterized by four dominant paradigms: text analysis (information extraction and classification), social network analysis (graph theory), social complexity analysis (complex systems science), and social simulations (cellular automata and agent-based modeling). However, when it comes to organizational and societal units of analysis, there exists no approach to conceptualize, model, analyze, explain, and predict social media interactions as individuals' associations with ideas, values, identities, and so on. To address this limitation, based on the sociology of associations and the mathematics of set theory, this paper presents a new approach to big data analytics called social set analysis. Social set analysis consists of a generative framework for the philosophies of computational social science, theory of social data, conceptual and formal models of social data, and an analytical framework for combining big social data sets with organizational and societal data sets. Three empirical studies of big social data are presented to illustrate and demonstrate social set analysis in terms of fuzzy set-theoretical sentiment analysis, crisp set-theoretical interaction analysis, and event-studies-oriented set-theoretical visualizations. Implications for big data analytics, current limitations of the set-theoretical approach, and future directions are outlined.
    Current analytical approaches in computational social science can be characterized by four dominant paradigms: text analysis (information extraction and classification), social network analysis (graph theory), social complexity analysis (complex systems science), and social simulations (cellular automata and agent-based modeling). However, when it comes to organizational and societal units of analysis, there exists no approach to conceptualize, model, analyze, explain, and predict social media interactions as individuals' associations with ideas, values, identities, and so on. To address this limitation, based on the sociology of associations and the mathematics of set theory, this paper presents a new approach to big data analytics called social set analysis. Social set analysis consists of a generative framework for the philosophies of computational social science, theory of social data, conceptual and formal models of social data, and an analytical framework for combining big social data sets with organizational and societal data sets. Three empirical studies of big social data are presented to illustrate and demonstrate social set analysis in terms of fuzzy set-theoretical sentiment analysis, crisp set-theoretical interaction analysis, and event-studies-oriented set-theoretical visualizations. Implications for big data analytics, current limitations of the set-theoretical approach, and future directions are outlined.
    LanguageEnglish
    Article number7462188
    JournalIEEE Access
    Volume4
    Pages2542-2571
    ISSN2169-3536
    DOIs
    StatePublished - 2016

    Keywords

    • Big data visual analytics
    • Big social data
    • Formal models
    • New computational models for big social data
    • Social set analysis

    Cite this

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    title = "Social Set Analysis: A Set Theoretical Approach to Big Data Analytics",
    abstract = "Current analytical approaches in computational social science can be characterized by four dominant paradigms: text analysis (information extraction and classification), social network analysis (graph theory), social complexity analysis (complex systems science), and social simulations (cellular automata and agent-based modeling). However, when it comes to organizational and societal units of analysis, there exists no approach to conceptualize, model, analyze, explain, and predict social media interactions as individuals' associations with ideas, values, identities, and so on. To address this limitation, based on the sociology of associations and the mathematics of set theory, this paper presents a new approach to big data analytics called social set analysis. Social set analysis consists of a generative framework for the philosophies of computational social science, theory of social data, conceptual and formal models of social data, and an analytical framework for combining big social data sets with organizational and societal data sets. Three empirical studies of big social data are presented to illustrate and demonstrate social set analysis in terms of fuzzy set-theoretical sentiment analysis, crisp set-theoretical interaction analysis, and event-studies-oriented set-theoretical visualizations. Implications for big data analytics, current limitations of the set-theoretical approach, and future directions are outlined.",
    keywords = "Big data visual analytics, Big social data, Formal models, New computational models for big social data, Social set analysis, Big data visual analytics, Big social data, Formal models, New computational models for big social data, Social set analysis",
    author = "Ravi Vatrapu and Mukkamala, {Raghava Rao} and Abid Hussain and Benjamin Flesch",
    year = "2016",
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    language = "English",
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    Social Set Analysis : A Set Theoretical Approach to Big Data Analytics. / Vatrapu, Ravi; Mukkamala, Raghava Rao; Hussain, Abid; Flesch, Benjamin.

    In: IEEE Access, Vol. 4, 7462188, 2016, p. 2542-2571.

    Research output: Contribution to journalJournal articleResearchpeer-review

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