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Social Structuration Online: Entropy and Social Systems

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Structural Differentiation in Social Media

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

One of the most important goals of the present volume is to define and relate group structuration to other online organizational and interactional phenomena. Although structuration is a high-level concept that may hold different meanings for different people, within this research, the concept is quite simple and clear. In brief, structuration is equated with the concept of “signal” in information systems, as defined by Shannon and Weaver (1948). Structuration is meaningful order, so by Shannon’s logic, structure is the opposite of entropy. Since structure is measured using entropy, we may say that structure increases as the observed value of entropy decreases. Conceptually, this means that structure is captured in the negative by observing the degree to which the system is not random (noisy or disordered).

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Notes

  1. 1.

    The present chapter expands and adapts Matei et al. (2010).

  2. 2.

    Wiki is the Hawaiian word for “quick” (Andrews 1865), implying the speed with which changes can be implemented (Cunningham 2005).

  3. 3.

    The present example employs logarithms taken at base 2, which allows entropy to be measured in bits. Other values such as e or 10 could be used for the base instead, as this choice is relatively arbitrary and does not affect the calculation of normalized entropy values or any proportional comparisons between contributors and contributions (Lemay 1999).

  4. 4.

    As an alternative to the number of words added, deleted, or modified, one could instead choose to use the number of characters as a measure of the amount of information contributed. The choice of measures will be addressed at greater length in Chap. 5.

  5. 5.

    On Wikipedia, registered users have their own screen name that appears in the history page for every article. Peripheral users with no membership have no screen name. However, we may still determine who wrote what using anonymous users’ IP addresses, which the wiki makes visible.

  6. 6.

    Measuring visible text is a relatively straightforward task. In contrast, it is challenging to determine how to count and measure the effort and/or impact of visual content and interactive media such as diagrams, photographs, videos, hyperlinks, and so forth. This conceptual and technical question calls for further scholarly attention and discussion; it is not addressed at length in this volume. The present study considers only visible text in calculations of effort and entropy, as well as various other measures to be discussed later in this volume.

  7. 7.

    Note that the first version was skipped, as normalized entropy cannot be calculated when only one user has contributed since the maximum possible entropy value would be H max = log2 1 = 0.

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Authors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Sorin Adam Matei

  2. South Dakota State University, Brookings, SD, USA

    Brian C. Britt

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  1. Sorin Adam Matei
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Matei, S.A., Britt, B.C. (2017). Social Structuration Online: Entropy and Social Systems. In: Structural Differentiation in Social Media. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-64425-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-64425-7_4

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