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Cyberspace reloaded: settlement size and distance in an online social network landscape


  • Balazs Lengyel


  • Akos Jakobi



This initial paper of our interest on geography of online social network is based on a literature in which geographers reformulated major concepts and hypotheses in the '90ies due to revolutionary development of internet (Cairncross, 1997). Cyberspace quickly became central issue in understanding human behaviour in the virtual world and cyber world has been always claimed to strongly twitted with physical world (Hayes, 1997). Parallel shift in economic geography research moved the focus of interest from distance to proximity, which is essential in our understanding for new knowledge creation and innovation in cities while the importance of distance is decreasing (Boschma, 2005). Economic geographers also claim that innovation and knowledge creation remained local in the era of internet because the need of face-to-face interactions (Feldman, 2002); internet-based communication seems to stimulate local offline communication (Storper and Venables, 2004). Social network sites are major fields of online communication and "enable users to articulate and make visible their social networks" (boyd and Ellison, 2007). Online social network (OSN) are large-scale networks and claimed to be supplemental forms of communication between people who have known each other primarily in real life (Ellison et al, 2006, 2007). We believe that studying these networks will give new insights to local learning and social capital issues by providing excellent data on online local learning and also proxies of offline local learning. According to recent findings on large scale OSNs (Facebook and Twitter), geographical location of users and their friends turns to be a determining factor for the structure of the network (Backstrom et al, 2011, Takhteyev et al, 2012, Ugander et al, 2011). However, more traditional geographical aspects are also needed to analyse spatial distribution of OSN activity. Our research questions address both the effects of distance and settlement size on population shares involved in online communities such as online social networks. Preliminary findings on iWiW, a leading online social network in Hungary with more than 4 million users, suggest that share of users is higher in bigger settlements and positively associated with geographical proximity of Budapest. On the other hand, the average number of friendship ties is independent from settlement size and is higher in peripheral regions of the country. In sum, settlement size and distance may play decisive role in shaping geographies of OSN. Keywords: online social network, geography, settlements, size effect, distance JEL codes: L86, R10, O18, O33

Suggested Citation

  • Balazs Lengyel & Akos Jakobi, 2012. "Cyberspace reloaded: settlement size and distance in an online social network landscape," ERSA conference papers ersa12p1032, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa12p1032

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    References listed on IDEAS

    1. Jesús Crespo Cuaresma & Martin Feldkircher, 2013. "Spatial Filtering, Model Uncertainty And The Speed Of Income Convergence In Europe," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 720-741, June.
    2. Jesús Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2014. "The Determinants of Economic Growth in European Regions," Regional Studies, Taylor & Francis Journals, vol. 48(1), pages 44-67, January.
    3. Manfred M. Fischer & Daniel A. Griffith, 2008. "Modeling Spatial Autocorrelation In Spatial Interaction Data: An Application To Patent Citation Data In The European Union," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 969-989.
    4. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    5. Fischer, Manfred M. & Scherngell, Thomas & Reismann, Martin, 2008. "Knowledge spillovers and total factor productivity. Evidence using a spatial panel data model," MPRA Paper 77762, University Library of Munich, Germany.
    6. Daniel A. Griffith, 2000. "A linear regression solution to the spatial autocorrelation problem," Journal of Geographical Systems, Springer, vol. 2(2), pages 141-156, July.
    7. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
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    JEL classification:

    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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