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A Bayesian Computational Model of Social Capital in Virtual Communities

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Springer London, Dordrecht Amsterdam


The theory of social capital (SC) is frequently discussed in the social sciences and the humanities. There is a plethora of research studies, which seek to define and empirically test the idea of SC in a number of ways. This growing body of research has only supported the significance of (SC) in physical communities. While many attempts have been made to examine different forms of social capital in physical communities, its application to other types of communities remains open to research. Recent interest in computer science and information systems in studying virtual communities (VCs) and the value these communities provide to information exchange and knowledge construction makes examination of SC in these communities relevant. We begin our understanding of SC in VCs by mapping out different variables that constitute SC based on qualitative experts’ knowledge of SC. We then develop an initial computational model of SC, and generate conditional probability tables (CPTs) that can be refined using real world case scenarios developed by experts in virtual communities. The Bayesian model seems to represent the situations mentioned in the paper adequately. This model provides a useful tool for understanding of SC in VCs.


Kei Daniel, B.; Zapata-Rivera, J.-D.; McCalla, G. (2003): A Bayesian Computational Model of Social Capital in Virtual Communities. Communities and Technologies: Proceedings of the First International Conference on Communities and Technologies 2003. DOI: 10.1007/978-94-017-0115-0_15. Springer London, Dordrecht Amsterdam. ISBN: 978-94-017-0115-0. pp. 287-305. Full Papers