Discovering Multi-Scale Community Structures from the Interpersonal Communication Network on Twitter

Conference paper
Part of the Advances in Geographic Information Science book series (AGIS)

Abstract

Despite the controversies of privacy and ethics, spatially-embedded communication data from widespread and emerging online social networks provide an unprecedented opportunity to study human interactions at the global scale. Detecting communities of individuals who live close by and have strong communication among each other is critical for a variety of application areas such as managing disaster response, controlling disease spread, and developing sustainable urban spaces and infrastructure. The ease of long-distance travel and communication have generated a highly complex network of human interactions, in which long-distance and short-distance ties coexist in multiple scales. Also, there is a hierarchical spatial organization in human interaction networks which reflect historic and socio-political borders. Patterns of human connectivity cross these historic and socio-political borders at multiple geographic scales. Therefore, a comprehensive understanding of human interactions necessitates analysis methods to take into account the complexity introduced by the multi-scale nature of human connectivity. This paper employs a spatially-constrained hierarchical regionalization algorithm to reveal multi-scale community structures in the interpersonal communication network on Twitter. The interpersonal communication network was constructed using a year of reciprocal and geo-located mention tweets in the U.S. between August 2015 and 2016. The results strikingly showed nested borders of cohesive regions at multiple scales, which are inherent to human communication patterns in the regional hierarchy of the U.S. Unsurprisingly, people communicated with others that live nearby, and multi-scale regions overlap with administrative boundaries of the states, cultural and dialectal regions, and topographical features. Furthermore, visualization of interregional communication patterns revealed a variety of spatial connectivity patterns such as poly-centricity, hierarchies, and spanning trees. Discovery of such patterns is essential for understanding of the complex social system that is influenced by long-distance ties.

Keywords

Community detection Hierarchical regionalization Interpersonal communication Twitter mentions Geo-social networks 

References

  1. 1.
    Backstrom L, Sun E, Marlow C (2010) Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th international conference on World wide web. pp. 61–70. ACMGoogle Scholar
  2. 2.
    Mok D, Wellman B, Carrasco J (2010) Does distance matter in the age of the Internet? Urban Stud 47:2747–2783CrossRefGoogle Scholar
  3. 3.
    Garcia-Gavilanes R, Mejova Y, Quercia D (2014) Twitter ain’t without frontiers: economic, social, and cultural boundaries in international communication. In: Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, p. 1511–1522. ACM, Baltimore, Maryland, USAGoogle Scholar
  4. 4.
    Takhteyev Y, Gruzd A, Wellman B (2012) Geography of Twitter networks. Soc Networks 34:73–81CrossRefGoogle Scholar
  5. 5.
    Yardi S, Boyd D (2010) Tweeting from the Town Square: Measuring geographic local networks. In: ICWSMGoogle Scholar
  6. 6.
    Leskovec J, Horvitz E (2014) Geospatial structure of a planetary-scale social network. IEEE Trans Comput Soc Syst 1:156–163CrossRefGoogle Scholar
  7. 7.
    Park P, Weber I, Mejova Y, Macy M (2013) The mesh of civilizations and international email flows. In: WebSci 2013 Proceedings. ACMGoogle Scholar
  8. 8.
    Kylasa SB, Kollias G, Grama A Social ties and checkin sites: connections and latent structures in location based social networks. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, pp. 194–201. ACMGoogle Scholar
  9. 9.
    Guo D (2009) Flow mapping and multivariate visualization of large spatial interaction data. IEEE Trans Visual Comp Grap 15:1041–1048CrossRefGoogle Scholar
  10. 10.
    Sobolevsky S, Szell M, Campari R, Couronné T, Smoreda Z, Ratti C (2013) Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS One 8:e81707CrossRefGoogle Scholar
  11. 11.
    Chen Y, Xu J, Xu MZ (2015) Finding community structure in spatially constrained complex networks. Int J Geogr Inf Sci 29:889–911CrossRefGoogle Scholar
  12. 12.
    Ratti C, Sobolevsky S, Calabrese F, Andris C, Reades J, Martino M, Claxton R, Strogatz SH (2010) Redrawing the map of Great Britain from a network of human interactions. PLoS One 5:e14248CrossRefGoogle Scholar
  13. 13.
    Thiemann C, Theis F, Grady D, Brune R, Brockmann D (2010) The structure of borders in a small world. PLoS One 5:e15422CrossRefGoogle Scholar
  14. 14.
    Slater PB (1975) Hierarchical regionalization of RSFSR administrative units using 1966-69 migration data. Soviet Geog Rev. Transl 16:453–465Google Scholar
  15. 15.
    Grauwin S, Szell M, Sobolevsky S, Hövel P, Simini F, Vanhoof M, Smoreda Z, Barabási A-L, Ratti C (2017) Identifying and modeling the structural discontinuities of human interactions. Sci Rep 7:46677CrossRefGoogle Scholar
  16. 16.
    Deville P, Song CM, Eagle N, Blondel VD, Barabasi AL, Wang DS (2016) Scaling identity connects human mobility and social interactions. Proc Natl Acad Sci U S A 113:7047–7052CrossRefGoogle Scholar
  17. 17.
    Emmerich T, Bunde A, Havlin S, Li G, Li D (2013) Complex networks embedded in space: dimension and scaling relations between mass, topological distance, and Euclidean distance. Phys Rev. E 87:032802CrossRefGoogle Scholar
  18. 18.
    von Landesberger T, Brodkorb F, Roskosch P, Andrienko N, Andrienko G, Kerren A (2016) Mobilitygraphs: visual analysis of mass mobility dynamics via spatio-temporal graphs and clustering. IEEE Trans Vis Comput Graph 22:11–20CrossRefGoogle Scholar
  19. 19.
    McGee J, Caverlee JA, Cheng Z (2011) A geographic study of tie strength in social media. In: Proceedings of the 20th ACM international conference on Information and knowledge management, pp. 2333–2336. ACMGoogle Scholar
  20. 20.
    Krings G, Calabrese F, Ratti C, Blondel VD (2009) Urban gravity: a model for inter-city telecommunication flows. J Stat Mech Theory Exp 2009(07):L07003CrossRefGoogle Scholar
  21. 21.
    Lambiotte R, Blondel VD, De Kerchove C, Huens E, Prieur C, Smoreda Z, Van Dooren P (2008) Geographical dispersal of mobile communication networks. Phys A Statis Mech Appl 387:5317–5325CrossRefGoogle Scholar
  22. 22.
    Barnett I, Khanna T, Onnela J-P (2016) Social and spatial clustering of people at humanity’s largest gathering. PLoS One 11:e0156794CrossRefGoogle Scholar
  23. 23.
    Garcia-Gavilanes R, Quercia D, Jaimes A (2013) Cultural dimensions in twitter: time, individualism and power. In: International AAAI conference on weblogs and social mediaGoogle Scholar
  24. 24.
    Yamaguchi Y, Amagasa T, Kitagawa H (2013) Landmark-based user location inference in social media. In: Proceedings of the first ACM conference on Online social networks, pp. 223–234. ACMGoogle Scholar
  25. 25.
    Jurgens D (2013) That’s what friends are for: inferring location in online social media platforms based on social relationships. ICWSM 13:273–282Google Scholar
  26. 26.
    Compton R, Jurgens D, Allen D (2014) Geotagging one hundred million twitter accounts with total variation minimization. In: 2014 IEEE international conference on big data (big data), pp. 393–401. IEEEGoogle Scholar
  27. 27.
    HerdaĞdelen A, Zuo W, Gard-Murray A, Bar-Yam Y (2013) An exploration of social identity: the geography and politics of news-sharing communities in twitter. Complexity 19:10–20CrossRefGoogle Scholar
  28. 28.
    Groh G, Straub F, Eicher J, Grob D (2014) Geographic aspects of tie strength and value of information in social networking. p. 1–10. ACMGoogle Scholar
  29. 29.
    Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99:7821–7826CrossRefGoogle Scholar
  30. 30.
    Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev. E 70:066111CrossRefGoogle Scholar
  31. 31.
    Hawelka B, Sitko I, Beinat E, Sobolevsky S, Kazakopoulos P, Ratti C (2014) Geo-located Twitter as proxy for global mobility patterns. Cartogr Geogr Inf Sci 41:260–271CrossRefGoogle Scholar
  32. 32.
    Nelson GD, Rae A (2016) An economic geography of the United States: from commutes to megaregions. PLoS One 11:e0166083CrossRefGoogle Scholar
  33. 33.
    Kallus Z, Barankai N, Szule J, Vattay G (2015) Spatial fingerprints of community structure in human interaction network for an extensive set of large-scale regions. PLoS One 10:e0126713CrossRefGoogle Scholar
  34. 34.
    Wang F, Mack EA, Maciewjewski R (2017) Analyzing entrepreneurial social networks with big data. Ann Am Assoc Geog 107:130–150Google Scholar
  35. 35.
    Sobolevsky S, Sitko I, des Combes RT, Hawelka B, Arias JM, Ratti C (2014) Money on the move: big data of bank card transactions as the new proxy for human mobility patterns and regional delineation. In: The case of residents and foreign visitors in Spain. 2014 IEEE international congress on big data (bigdata congress), pp. 136–143Google Scholar
  36. 36.
    Croitoru A, Wayant N, Crooks A, Radzikowski J, Stefanidis A (2015) Linking cyber and physical spaces through community detection and clustering in social media feeds. Comput Environ Urban Syst 53:47–64CrossRefGoogle Scholar
  37. 37.
    Gao S, Liu Y, Wang Y, Ma X (2013) Discovering spatial interaction communities from mobile phone data. Trans GIS 17:463–481CrossRefGoogle Scholar
  38. 38.
    Stefanidis A, Cotnoir A, Croitoru A, Crooks A, Rice M, Radzikowski J (2013) Demarcating new boundaries: mapping virtual polycentric communities through social media content. Cartogr Geogr Inf Sci 40:116–129CrossRefGoogle Scholar
  39. 39.
    Lansley G, Longley PA (2016) The geography of Twitter topics in London. Comput Environ Urban Syst 58:85–96CrossRefGoogle Scholar
  40. 40.
    Guo D (2008) Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). Int J Geogr Inf Sci 22:801–823CrossRefGoogle Scholar
  41. 41.
    Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev. E 69:026113CrossRefGoogle Scholar
  42. 42.
    Bachi R (1973) Geostatistical analysis of territories. Bull Int Stat Ins 45:121–133Google Scholar
  43. 43.
    Blair D, Biss T (1967) The measurement of shape in geography: an appraisal of methods and techniques. Bulletin of Quantitative Data for Geographers. p 45Google Scholar
  44. 44.
    MacEachren AM (1985) Compactness of geographic shape: comparison and evaluation of measures. Geografiska Ann Ser B Human Geogr 67:53CrossRefGoogle Scholar
  45. 45.
    Cogan P, Andrews M, Bradonjic M, Kennedy WS, Sala A, Tucci G Reconstruction and analysis of twitter conversation graphs. In: Proceedings of the First ACM international workshop on hot topics on interdisciplinary social networks research, pp. 25–31. ACMGoogle Scholar
  46. 46.
    Pavalanathan, U., Eisenstein, J. (2015) Confounds and consequences in geotagged twitter data. arXiv:1506.02275Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Geographical and Sustainability SciencesUniversity of IowaIowa CityUSA

Personalised recommendations