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The Impact of Geographic Distance on Online Social Interactions

Abstract

Online social networking services entice millions of users to spend hours every day interacting with each other. The focus of this work is to explain the effect that geographic distance has on online social interactions and, simultaneously, to understand the interplay between the social characteristics of friendship ties and their spatial properties. We analyze data from a large-scale online social network, Tuenti, with about 10 million active users: our sample includes user profiles, user home locations and online social interactions among Tuenti members. Our findings support the idea that spatial distance constraints whom users interact with, but not the intensity of their social interactions. Furthermore, friendship ties belonging to denser connected groups tend to arise at shorter spatial distances than social ties established between members belonging to different groups. Finally, we show that our findings mostly do not depend on the age of the users, although younger users seem to be slightly more constrained to shorter geographic distances. Augmenting social structure with geographic information adds a new dimension to social network analysis and a large number of theoretical investigations and practical applications can be pursued for online social systems, with many promising outcomes. As the amount of available location-based data is increasing, our findings and results open the door to future possibilities: researchers would benefit from these insights when studying online social services, while developers should be aware of these additional possibilities when building systems and applications related to online social platforms.

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Notes

  1. 1.

    Data collected by November, 2010.

  2. 2.

    www.tuenti.com

  3. 3.

    This requirement was later changed, but after our dataset was collected.

  4. 4.

    In other words all users are assigned to a city (represented by a point)

  5. 5.

    en.wikipedia.org/wiki/Spanish_naming_customs

  6. 6.

    The shortest distance between two points on the surface of a sphere measured along the surface of the sphere: \(d_{ij}=r\arccos (\sin \phi _{i}\sin \phi _{j}+\cos (\lambda _{i}-\lambda _{j})\cos \phi _{i}\cos \phi _{j}),\) where ϕ i , λ i and ϕ j , λ j are geographic latitude and longitude cities of user i and j. We use the mean Earth radius r ≈ 6378km.

References

  1. Adamic, L.A., Buyukkokten, O., & Adar, E. (2003). A social network caught in the web. First Monday, 8(6).

  2. Ahn, Y.Y., Han, S., Kwak, H., Moon, S., & Jeong, H. (2007). Analysis of topological characteristics of huge online social networking services. In Proceedings of WWW’ 07 (pp. 835–844). New York: ACM.

    Chapter  Google Scholar 

  3. Backstrom, L., Sun, E., & Marlow, C. (2010a). Find me if you can: improving geographical prediction with social and spatial proximity. In Proceedings of WWW ’10 (pp. 61–70).

  4. Bakshy, E., Rosenn, I., Marlow, C.A., & Adamic, L.A. (2012). The role of social networks in information diffusion. In Proceedings of the 21st world wide web conference (WWW 2012) Lyon.

  5. Barthélemy, M. (2011). Spatial networks. Physics Reports, 499, 1–101.

    Article  Google Scholar 

  6. Bastos, M.T., da Cunha Recuero, R., & da Silva Zago, G. (2014). Taking tweets to the streets: a spatial analysis of the vinegar protests in brazil. First Monday, 19(3).

  7. Bell, M., Charles-Edwards, E., Ueffing, P., Stillwell, J., Kupiszewski, M., & Kupiszewska, D. (2015). Internal migration and development: comparing migration intensities around the world. Population and Development Review, 41(1), 33–58.

    Article  Google Scholar 

  8. Borge-Holthoefer, J., Rivero, A., García, I., Cauhé, E., Ferrer, A., Ferrer, D., Francos, D., Iniguez, D., Pérez, M.P., Ruiz, G., & et al (2011). Structural and dynamical patterns on online social networks: the spanish may 15th movement as a case study. PloS one, 6(8), e23–883.

    Google Scholar 

  9. Cairncross, F. (2001). The death of distance: how the communications revolution is changing our lives. Cambridge: Harvard Business School Press.

    Google Scholar 

  10. Castells, M. (2008). The new public sphere: global civil society, communication networks, and global governance. The aNNalS of the american academy of Political and Social Science, 616(1), 78–93.

    Article  Google Scholar 

  11. Cha, M., Haddadi, H., Benevenuto, F. , & Gummadi, K.P. (2010). Measuring user influence in twitter: the million follower fallacy. In Proceedings of ICWSM ’10. http://aaai.org/ocs/index.php/ICWSM/ICWSM10/paper/view/1538/0.

  12. Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? predicting political orientation and measuring political homophily in twitter using big data. Journal of Communication, 64(2), 317–332.

    Article  Google Scholar 

  13. Conover, M.D., Davis, C., Ferrara, E., McKelvey, K., Menczer, F., & Flammini, A. (2013). The geospatial characteristics of a social movement communication network. PloS one, 8(3), e55–957.

    Article  Google Scholar 

  14. Dindia, K., & Canary, D.J. (1993). Definitions and theoretical perspectives on maintaining relationships. Journal of Social and Personal Relationships, 10(2), 163–173. http://doi.org/10.1177/026540759301000201. http://spr.sagepub.com/content/10/2/163.short, http://spr.sagepub.com/content/10/2/163.full.pdf+html.

    Article  Google Scholar 

  15. Donath, J. , & Boyd, D. (2004). Public Displays of Connection. BT Technology Journal, 22(4), 71–82. http://doi.org/10.1023/B:BTTJ.0000047585.06264.cc.

    Article  Google Scholar 

  16. Dunbar, R. (1998). Grooming, Gossip, and the Evolution of Language. Harvard University Press.

  17. Evans, B.M., & Chi, E.H. (2008). Towards a model of understanding social search. In Proceedings of the 11th ACM conference on computer supported cooperative work (CSCW 2008) (pp. 485–494). San Diego: ACM.

    Google Scholar 

  18. Expert, P., Evans, T.S., Blondel, V.D., & Lambiotte, R. (2011). Uncovering space-independent communities in spatial networks. Proceedings of the National Academy of Sciences, 108(19), 7663–7668.

    Article  Google Scholar 

  19. Friedkin, N. (1980). A test of structural features of granovetter’s strength of weak ties theory. Social networks, 2(4), 411–422.

    Article  Google Scholar 

  20. Garriss, S., Kaminsky, M. , Freedman, M.J., Karp, B., Mazières, D. , & Yu, H. (2006). RE: Reliable Email. In Proceedings of the third Symposium on Networked Systems Design and Implementation (NSDI ’06) (pp 297–310).

  21. Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp, 211–220). ACM.

  22. Golbeck, J. (2008). Weaving a web of trust. Science, 321(5896), 1640–1641.

    Article  Google Scholar 

  23. Goldenberg, J., & Levy, M. (2009). Distance Is Not Dead: Social Interaction and Geographical Distance in the Internet Era. arXiv:0906.3202.

  24. Granovetter, M.S. (1973). The strength of weak ties. The American Journal of Sociology, 78(6), 1360–1380. https://doi.org/10.2307/2776392.

    Article  Google Scholar 

  25. Hecht, B. , Hong, L. , Suh, B., & Chi, E.H. (2011). Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. In Proceedings of CHI ’11.

  26. Horowitz, D., & Kamvar, S.D. (2010). The anatomy of a large-scale social search engine. In Proceedings of the 19th world wide web conference (WWW 2010) ACM, Raleigh North Carolina.

  27. Jiang, J., Wilson, C., Wang, X., Huang, P., Sha, W., Dai, Y., & Zhao, B.Y. (2010). Understanding latent interactions in online social networks. In Proceedings of IMC ’10 (pp. 369–382). New York: ACM. https://doi.org/10.1145/1879141.1879190.

    Chapter  Google Scholar 

  28. Jurgens, D., McCorriston, J., Xu, Y.T. , & Ruths, D. (2015). Geolocation prediction in twitter using social networks: A critical analysis and review of current practice. In Proceedings of the 9th international AAAI conference on weblogs and social media (ICWSM).

  29. Kaltenbrunner, A., Gonzalez, G., Ruiz De Querol, R., & Volkovich, Y. (2011). Comparative analysis of articulated and behavioural social networks in a social news sharing website. New Review of Hypermedia and Multimedia, 17(3), 243–266. https://doi.org/10.1080/13614568.2011.598192.

    Article  Google Scholar 

  30. Kaltenbrunner, A., Scellato, S., Volkovich, Y., Laniado, D., Currie, D., Jutemar, E.J., & Mascolo, C. (2012). Far from the eyes, close on the web: impact of geographic distance on online social interactions. In Proceedings of the 2012 ACM workshop on online social networks (pp. 19–24). ACM.

  31. Kleinberg, J.M. (2000). Navigation in a small world. Nature, 406(6798), 845. https://doi.org/10.1038/35022643.

    Article  Google Scholar 

  32. Krackhardt, D., & Kilduff, M. (1999). Whether close or far: Social distance effects on perceived balance in friendship networks. Journal of personality and social psychology, 76(5), 770.

    Article  Google Scholar 

  33. Kumar, R., Novak, J., & Tomkins, A. (2006). Structure and evolution of online social networks. In Proceedings of KDD ’06 (pp. 611–617). New York: ACM. https://doi.org/10.1145/1150402.1150476.

    Chapter  Google Scholar 

  34. Kwak, H., Lee, C., Park, H., & Moon, S. (2010b). What is Twitter, a social network or a news media? In Proceedings of the 19th international World Wide Web conference (pp. 591–600). ACM.

  35. Lambiotte, R., Blondel, V., Dekerchove, C., Huens, E., Prieur, C., Smoreda, Z., & Vandooren, P. (2008). Geographical dispersal of mobile communication networks. Physica A, 387(21), 5317–5325. https://doi.org/10.1016/j.physa.2008.05.014.

    Article  Google Scholar 

  36. Laniado, D., Volkovich, Y., Kappler, K., & Kaltenbrunner, A. (2016). Gender homophily in online dyadic and triadic relationships. EPJ Data Science, 5(1), 1.

    Article  Google Scholar 

  37. Leskovec, J., & Horvitz, E. (2008). Planetary-scale views on a large instant-messaging network. In Proceedings of WWW’ 08.

  38. Liben-Nowell, D., Novak, J., Kumar, R., Raghavan, P., & Tomkins, A. (2005). Geographic routing in social networks. PNAS, 102(33), 11,623–11,628. https://doi.org/10.1073/pnas.0503018102.

    Article  Google Scholar 

  39. Livingstone, S., Ólafsson, K., & Staksrud, E. (2013). Risky social networking practices among underage users: lessons for evidence-based policy. Journal of Computer-Mediated Communication, 18(3), 303–320. https://doi.org/10.1111/jcc4.12012.

    Article  Google Scholar 

  40. Mascolo, C. (2010). The power of mobile computing in a social era. Internet Computing, 14(6), 76–79.

    Article  Google Scholar 

  41. McGee, J., Caverlee, J. , & Cheng, Z. (2013). Location prediction in social media based on tie strength. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management (pp 459–468). ACM .

  42. Milgram, S. (1977). The familiar stranger: an aspect of urban anonymity. Cambridge: Addison-Wesley.

    Google Scholar 

  43. Miritello, G., Moro, E., & Lara, R. (2011). Dynamical strength of social ties in information spreading. Physical Review E, 83, 045–102. https://doi.org/10.1103/PhysRevE.83.045102.

    Article  Google Scholar 

  44. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., & Bhattacharjee, B. (2007). Measurement and analysis of online social networks. In Proceedings of IMC ’07 (pp. 29–42). New York: ACM. https://doi.org/10.1145/1298306.1298311. http://portal.acm.org/citation.cfm?id=1298306.1298311.

    Chapter  Google Scholar 

  45. Mok, D., Wellman, B., & Carrasco, J.A. (2009). Does distance still matter in the age of the Internet? Urban Studies, 46(13), 2747–2783.

    Article  Google Scholar 

  46. Newman, M., Barabasi, A.L., & Watts, D.J. (2006). The Structure and Dynamics of Networks, 1st edn. Princeton Studies in Complexity: Princeton University Press.

    Google Scholar 

  47. Onnela, J.P., Arbesman, S., González, M.C., Barabási, A.L., & Christakis, N.A. (2011). Geographic constraints on social network groups. PLoS ONE, 6(4), e16,939. https://doi.org/10.1371/journal.pone.0016939.

    Article  Google Scholar 

  48. Rogers, E. (1995). Diffusion of innovations. New York: Free Press.

    Google Scholar 

  49. Rout, D., Bontcheva, K., Preoţiuc-Pietro, D., & Cohn, T. (2013). Where’s@ wally?: a classification approach to geolocating users based on their social ties. In Proceedings of the 24th ACM Conference on Hypertext and Social Media (pp. 11–20). ACM .

  50. Sadilek, A., Kautz, H., & Bigham, J.P. (2012). Finding your friends and following them to where you are. In Proceedings of the fifth ACM international conference on Web search and data mining (pp. 723–732). ACM.

  51. Scellato, S., Mascolo, C., Musolesi, M., & Latora, V. (2010). Distance matters: geo-social metrics for online social networks. In Proceedings of WOSN’10.

  52. Scellato, S., Mascolo, C., Musolesi, M., & Crowcroft, J. (2011a). Track globally, deliver locally: improving content delivery networks by tracking geographic social cascades. In Proceedings of the 20th world wide web conference (WWW’11) Hyderabad, India.

  53. Scellato, S, Noulas, A, Lambiotte, R, & Mascolo, C (2011b). Socio-Spatial properties of online location-based social networks. In Proceedings of ICWSM’11.

  54. Spiro, E.S., Almquist, Z.W., & Butts, C.T. (2016). The persistence of division: geography, institutions, and online friendship ties. Socius: Sociological Research for a Dynamic World, 2, 2378023116634,340.

    Google Scholar 

  55. Telefónica, F. (2012). La Sociedad de la Información en España 2011. Fundación Telefónica.

  56. Tobler, W.R. (1970). A computer movie simulating urban growth in the detroit region. Economic Geography, 46, 234–240. https://doi.org/10.2307/143141.

    Article  Google Scholar 

  57. Travers, J., & Milgram, S. (1967). The small world problem. Phychology Today, 1, 61–67.

    Google Scholar 

  58. Traverso, S., Huguenin, K., Erramilli, T.V.I., Laoutaris, N., & Papagiannaki, K. (2012). Tailgate: handling long-tail content with a little help from friends. In Proceedings of the 21st world wide web conference (WWW 2012), Lyon, France.

  59. Ugander, J., Karrer, B., Backstrom, L., & Marlow, C. (2011). The anatomy of the facebook social graph. arXiv:11114503.

  60. Volkovich, Y., Scellato, S., Laniado, D., Mascolo, C., & Kaltenbrunner, A. (2012). The length of bridge ties: structural and geographic properties of online social interactions. In International AAAI conference on weblogs and social media (ICWSM-12).

  61. Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P.N., & Zhao, B.Y. (2009). User interactions in social networks and their implications. In Proceedings of eurosys ’09 (pp. 205–218). New York: ACM.

    Google Scholar 

  62. Zipf, G.K. (1948). Human behaviour and the principle of least effort. Cambridge, MA: Addison-Wesley.

    Google Scholar 

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Laniado, D., Volkovich, Y., Scellato, S. et al. The Impact of Geographic Distance on Online Social Interactions. Inf Syst Front 20, 1203–1218 (2018). https://doi.org/10.1007/s10796-017-9784-9

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Keywords

  • Online social networks
  • Geographic properties
  • Online interactions
  • User behavior
  • Age factors