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Viable Web Communities: Two Case Studies

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Viability and Resilience of Complex Systems

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

Addressing the question of what makes an online social system “viable” requires some preliminary conceptual clarifications in order to define the scope of the present analysis. Section 4.1 of the present chapter is devoted to framing the problem conceptually: we first introduce the notion of a collaborative Web community by considering the properties that characterise it; we then discuss a number of ways in which the viability of these systems can be defined and the challenges faced by empirical research in identifying measurable indicators of viability. In Sect. 4.2 we present an empirical analysis of two paradigmatic cases of collaborative Web communities and discuss methodological issues emerging from the study of their dynamics from the point of view of viability. We conclude by presenting in Sect. 4.3 a simple model of viable online communities based on the empirical and conceptual considerations of the first two sections.

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Notes

  1. 1.

    We will use in what follows “online groups” and “Web communities” as synonyms to refer to social systems that require an explicit act of affiliation for users to become members, as opposed to communities that can be “detected” on the basis of topological properties of a network (see for example Edling 2002; Newman 2006).

  2. 2.

    The question of the mapping between real, offline identities and online identities in Web communities is beyond the scope of the present discussion. In our work we refer to a member of an online community as a user that can be identified by a unique online identifier, no matter who actually owns and controls that identifier.

  3. 3.

    For an example of qualitative, community driven attempts at understanding patterns that affect in a positive or negative way the evolution of a collaborative community, see Mader (2007).

  4. 4.

    For an extensive discussion of results presented in this section, see Roth et al. (2008).

  5. 5.

    As per our considerations in the first section of this chapter, it should be noted that sheer content growth per se may not be a good indicator of a sustainable wiki, as studies on wiki proliferation also seem to suggest Happel and Treitz (2008).

  6. 6.

    This initial dataset includes among others a large set of Wikipedias.

  7. 7.

    http://s23.org/wikistats/largesthtml.php The database is maintained by a user called “Mutante” who graciously granted us the permission to harvest this data.

  8. 8.

    http://flickr.com/services/api.

  9. 9.

    For a comparison with the idea of a sociodemographic space, see McPherson et al. (1992).

  10. 10.

    http://dev.nitens.org/flickr/grouptrackr.php.

  11. 11.

    A similar rationale for focussing on mid-size groups is in Backstrom et al. (2006).

  12. 12.

    It should be noted that, compared to other studies that considered a random sample of the global Flickr user and group population, our study focused on public groups (i.e. groups with public content, flagged as “safe” and hence open to recruit any Flickr user as a potential member) and users that engage in actual social activity such as being member of at least one public group (Prieur et al. 2008 estimated this to represent the 8% of the total Flickr population in 2006). This explains the mismatch between the global statistics reported by other studies that include private groups and non-social users (i.e. users who may only use social media services as a way to dump private content not meant for public consumption).

  13. 13.

    For the sake of differentiability, we obviously have to make a continuous approximation on U, P and A by considering that they evolve in ℝ; one may still consider that the population at t equals ⌊U t ⌋.

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Acknowledgements

This work was partly supported by the PATRES project (NEST-043268) funded by the FP6 programme of the European Commission and by the Future and Emerging Technologies programme FP7-COSI-ICT of the European Commission through project QLectives (grant 231200). We are grateful to Nigel Gilbert, Volker Grimm, Nic Geard, Przemek Grabowicz, as well as members of the Centre for Research in Social Simulation (University of Surrey) and participants in the 2008 Dagstuhl seminar “Social Web Communities” for valuable feedback and insights on earlier versions of this work.

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Authors and Affiliations

  1. Centre for Research in Social Simulation, University of Surrey, Guildford, GU2 7XH, Surrey, UK

    Dario Taraborelli

  2. Centre d’analyse et de mathématique sociales, CNRS-EHESS, 54, boulevard Raspail, 75006, Paris, France

    Camille Roth

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  1. Dario Taraborelli
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Correspondence to Camille Roth .

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    Guillaume Deffuant

  2. Social and Computer Sciences, Research Group, University Surrey, Guildford, Surrey, GU2 5XG, United Kingdom

    Nigel Gilbert

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Taraborelli, D., Roth, C. (2011). Viable Web Communities: Two Case Studies. In: Deffuant, G., Gilbert, N. (eds) Viability and Resilience of Complex Systems. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20423-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-20423-4_4

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