Advertisement

Geovisualising Unequal Spatial Distribution of Online Social Network Connections: A Hungarian Example

Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

With the help of geovisualisation and analytical techniques this paper aims to introduce how online social network (OSN) connections are shaped by geographical rules. The paper deals with methodological issues of mapping OSN data in a detailed manner on the level of settlements, which has been underexplored in previous spatial researches. Examples of iWiW, the largest Hungarian online social network, are applied to show unequal spatial distribution of city to city connections. The results reflect that geographical proximity is decisive also in virtual space connections, which could be traced on maps of network topology as well. Our calculations and geovisualised models additionally highlighted that population size and user intensity in settlements may have some relations, too. Therefore, maps of network connections have been carried out both with raw weights and after size normalization, which performed differently. Additionally the paper deals with methodological questions of mapping OSN data that were until now basically aspatial. Geolocalisation of data was made by joining user-given residence information with Hungarian settlement coordinates. The settlement level aggregated dataset then became possible to visualise relations, however, since total number of mappable connections reached more that 1.3 million, GIS techniques had to be applied to select and visualise the relevant information. Finally thematic maps were possible to be generated from the originally non-spatial dataset of the OSN regarding user rates or connectivity characteristics of Hungarian settlements. All in all, cartographic outcomes could serve as useful instruments to understand how network space relations are generated.

Keywords

Social Network Site Online Social Network Network Connection Voluntary Geographic Information Small Settlement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Research work of Ákos Jakobi was supported by the János Bolyai Scholarship of the Hungarian Academy of Sciences. Balázs Lengyel acknowledges financial support of IBS Research Grant.

References

  1. Acquisti A, Gross R (2009) Predicting social security numbers from public data. Proc Natl Acad Sci U S A 106(27):10975–10980CrossRefGoogle Scholar
  2. Ahn YY, Han S, Kwak H, Eom YH, Moon S, Jeong H (2007) Analysis of topological characteristics of huge online social networking services. In: Proceedings of the 16th international conference on World Wide Web. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.87.2864&rep=rep1&type=pdf. Accessed 1 Aug 2013
  3. Backstrom L, Boldi P, Rosa M, Ugander J, Vigna S (2012) Four degrees of separation. arXiv:1111.4570. http://arxiv.org/pdf/1111.4570v3.pdf. Accessed 14 Oct 2014
  4. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512CrossRefGoogle Scholar
  5. Boyd D (2008) Why youth ♥ social network sites: the role of networked publics in teenage social life. In: Buckingham D (ed) Youth, identity, and digital media. MIT, Cambridge, pp 119–142Google Scholar
  6. Boyd D, Ellison NB (2007) Social network sites: definition, history, and scholarship. J Comput Mediat Commun 13:210–230Google Scholar
  7. Buttler P (2010) Visualizing Facebook friends: eye candy in R. http://paulbutler.org/archives/visualizing-facebook-friends/. Accessed 13 Dec 2013
  8. Ellison N, Steinfield C, Lampe C (2006) Spatially bounded online social networks and social capital: the role of Facebook. Paper presented at the annual conference of the International Communication Association, 19–23 June. http://www.icahdq.org/conf/2006confprogram.asp
  9. Ellison N, Steinfield C, Lampe C (2007) The benefits of Facebook friends: social capital and college students’ use of online social network sites. J Comput Mediat Commun 12:1143–1168CrossRefGoogle Scholar
  10. Escher T (2007) The geography of online social networks. Presentation at Oxford Internet Institute September 5. http://people.oii.ox.ac.uk/escher/wp-content/uploads/2007/09/Escher_York_presentation.pdf. Accessed 6 Jul 2013
  11. Fischer E (2011) The geotaggers’ world atlas. http://www.flickr.com/photos/walkingsf/4621754005/in/set-72157623971287575. Accessed 6 Jul 2013
  12. Gätz T (2010) Facebook vs. the rest of the world. http://www.flickr.com/photos/thorstengaetz/5261467662/. Accessed 13 Dec 2013
  13. Graham M, Gaffney D (2012) Where do tweets come from? http://www.zerogeography.net/2012/04/where-do-tweets-come-from.html. Accessed 13 Dec 2013
  14. Graham M, Zook M (2011) Visualizing global cyberscapes: mapping user generated placemarks. J Urban Technol 18(1):115–132CrossRefGoogle Scholar
  15. Greenhow C (2011) Learning and social media: what are the interesting questions for research? Int J Cyber Behav Psychol Learn 1:36–50CrossRefGoogle Scholar
  16. Hecht B, Hong L, Suh B, Chi EH (2011) Tweets from Justin Bieber’s heart: the dynamics of the “location” field in user profiles. In: Proceedings of the ACM conference on human factors in computing systems, pp 237–246. ACM Press, New YorkGoogle Scholar
  17. Hogan B (2009) A comparison of on- and offline networks through the Facebook API. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1331029. Accessed 6 Jul 2013
  18. Kumar R, Novak J, Tomkins A (2006) Structure and evolution of online social networks. In: Proceedings of 12th international conference on knowledge discovery in data mining, pp 611–617. ACM Press, New YorkGoogle Scholar
  19. Lengyel B, Jakobi Á (2014) Online social networks and location: the dual effect of distance on spread and involvement. Tijdschrift voor Economische en Sociale Geografie (in press)Google Scholar
  20. Liben-Nowell D, Novak J, Kumar R, Raghavan P, Tomkins A (2005) Geographic routing in social networks. Proc Natl Acad Sci U S A 102:11623–11628Google Scholar
  21. Takhteyev Y, Gruzd A, Wellman B (2012) Geography of Twitter networks. Soc Netw 34:73–81CrossRefGoogle Scholar
  22. Traud A, Kelsic E, Mucha P, Porter M (2008) Community structure in online collegiate social networks. http://cds.cern.ch/record/1124061. Accessed 10 Jul 2013
  23. Ugander J, Karrer B, Backstrom L, Marlow C (2011) The anatomy of the Facebook social graph. http://arxiv.org/abs/1111.4503. Accessed 6 Jul 2013
  24. Warden P (2010) How to split up the US. http://petewarden.com/2010/02/06/how-to-split-up-the-us/. Accessed 13 Dec 2013
  25. Yardi S, Boyd D (2010) Tweeting from the town square: measuring geographic local networks. Paper presented at AAAI of the international conference on Weblogs and Social Media, 23–26 May. http://yardi.people.si.umich.edu/pubs/Yardi_TownSquare10.pdf. Accessed 11 Jul 2013
  26. Zhao S, Grasmuck S, Martin J (2008) Identity construction on Facebook: digital empowerment in anchored relationships. Comput Hum Behav 24:1816–1836CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Regional ScienceEötvös Loránd UniversityBudapestHungary
  2. 2.OSON Research LabInternational Business School BudapestBudapestHungary
  3. 3.Centre for Economic and Regional StudiesHungarian Academy of SciencesBudapestHungary

Personalised recommendations