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Dynamics of personal social relationships in online social networks: a study on twitter

Published:07 October 2013Publication History

ABSTRACT

The growing popularity of Online Social Networks (OSN) is generating a large amount of communication records that can be easily accessed and analysed to study human social behaviour. This represents a unique opportunity to understand properties of social networks that were impossible to assess in the past. Although analyses on OSN conducted hitherto revealed some important global properties of the networks, there is still a lack of understanding of the mechanisms underpinning these properties, their relation to human behaviour, and their dynamic evolution over time. These aspects are clearly important to understand and characterise OSN and to identify the evolutionary strategy that favoured the diffusion of the use of online communications in our society.

In this paper we analyse a data set of Twitter communication records, studying the dynamic processes that govern the maintenance of online social relationships. The results reveal that people in Twitter have highly dynamic social networks, with a large percentage of weak ties and high turnover. This suggests that this behaviour can be the product of an evolutionary strategy aimed at coping with the extremely challenging conditions imposed by our society, where dynamism seems to be the key to success.

References

  1. O. Aarts, P.-P. van Maanen, T. Ouboter, and J. M. Schraagen. Online Social Behavior in Twitter: A Literature Review. ICDMW, pages 739--746, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. V. Arnaboldi, M. Conti, A. Passarella, and F. Pezzoni. Analysis of Ego Network Structure in Online Social Networks. In SocialCom, pages 31--40, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. V. Arnaboldi, M. Conti, A. Passarella, and F. Pezzoni. Ego Networks in Twitter: an Experimental Analysis. In Netscicom, 2013.Google ScholarGoogle Scholar
  4. V. Arnaboldi, A. Guazzini, and A. Passarella. Egocentric Online Social Networks: Analysis of Key Features and Prediction of Tie Strength in Facebook. Computer Communications, 36(10--11):1130--1144, 2013.Google ScholarGoogle Scholar
  5. R. S. Burt. Structural Holes versus Network Closure as Social Capital. 2001.Google ScholarGoogle Scholar
  6. M. De Choudhury, N. Diakopoulos, and M. Naaman. Unfolding the event landscape on twitter: classification and exploration of user categories. In CSCW, pages 241--244, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. Delaney, N. Salminen, and E. Lee. Time americans spend per month on social media sites - sociallyawareblog.com, 2012.Google ScholarGoogle Scholar
  8. R. Dunbar. Theory of mind and the evolution of language. In Approaches to the Evolution of Language, chapter 6, pages 92--110. 1998.Google ScholarGoogle Scholar
  9. R. I. M. Dunbar. The social brain hypothesis. Evolutionary Anthropology, 6(5):178--190, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  10. D. Easley and J. Kleinberg. Networks, Crowds, and Markets: Reasoning about a highly connected world. 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. E. Gilbert. Predicting tie strength in a new medium. In CSCW, pages 1047--1056, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. E. Gilbert and K. Karahalios. Predicting tie strength with social media. In CHI, pages 211--220, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. B. Gonçalves, N. Perra, and A. Vespignani. Modeling users' activity on twitter networks: validation of Dunbar's number. PloS one, 6(8):e22656, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  14. N. Z. Gong, W. Xu, L. Huang, P. Mittal, E. Stefanov, V. Sekar, and D. Song. Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google. In IMC, pages 131--144, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. M. S. Granovetter. The Strength of Weak Ties. The American Journal of Sociology, 78(6):1360--1380, 1973.Google ScholarGoogle ScholarCross RefCross Ref
  16. R. A. Hill and R. I. M. Dunbar. Social network size in humans. Human Nature, 14(1):53--72, 2003.Google ScholarGoogle ScholarCross RefCross Ref
  17. R. Kumar, J. Novak, and A. Tomkins. Structure and evolution of online social networks. KDD, pages 611--617, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. J. Leskovec and E. Horvitz. Planetary-Scale Views on an Instant-Messaging Network. Technical report, 2007.Google ScholarGoogle Scholar
  19. G. Magno and G. Comarela. New kid on the block: Exploring the google social graph. In Proceedings of the ..., pages 159--170, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. P. V. Marsden and K. E. Campbell. Measuring Tie Strength. Social Forces, 63(2):482--501, 1984.Google ScholarGoogle ScholarCross RefCross Ref
  21. G. Miritello, R. Lara, M. Cebrian, and E. Moro. Limited communication capacity unveils strategies for human interaction. Scientific Reports, 3:1--7, June 2013.Google ScholarGoogle ScholarCross RefCross Ref
  22. M. Muller, D. R. Millen, N. S. Shami, and J. Feinberg. We are all Lurkers: Toward a Lurker Research Agenda. In CSCW, pages 1--10, 2010.Google ScholarGoogle Scholar
  23. D. Quercia, L. Capra, and J. Crowcroft. The social world of twitter: Topics, geography, and emotions. ..., AAAI Conference on Weblogs and Social ..., (Hansen 1999), 2012.Google ScholarGoogle Scholar
  24. J. Saramaki, E. Leicht, E. Lopez, S. Robetrs, F. Reed-Tsochas, and R. Dunbar. The persistence of social signatures in human communication. arXiv preprint arXiv, pages 1--16, 2012.Google ScholarGoogle Scholar
  25. A. Sutcliffe, R. Dunbar, J. Binder, and H. Arrow. Relationships and the social brain: Integrating psychological and evolutionary perspectives. British journal of psychology, 103(2):149--68, 2012.Google ScholarGoogle Scholar
  26. M. Sweney. Facebook sees first dip in UK users - guardian.co.uk, 2008.Google ScholarGoogle Scholar
  27. J. Travers and S. Milgram. An Experimental Study of the Small World Problem. Sociometry, 32(4):425, 1969.Google ScholarGoogle Scholar
  28. C. Wilson, A. Sala, K. P. N. Puttaswamy, and B. Y. Zhao. Beyond Social Graphs: User interactions in online social networks and their implications. ACM Transactions on the Web, 6(4):1--31, Nov. 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. B. Worthen. Bill Gates quits Facebook - Wall St. Journal Online, 2008.Google ScholarGoogle Scholar
  30. X. Zhao, A. Sala, C. Wilson, and X. Wang. Multi-scale dynamics in a massive online social network. In IMC, pages 171--184, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. W.-X. Zhou, D. Sornette, R. a. Hill, and R. I. M. Dunbar. Discrete hierarchical organization of social group sizes. In Biological sciences, volume 272, pages 439--44, 2005.Google ScholarGoogle Scholar

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      cover image ACM Conferences
      COSN '13: Proceedings of the first ACM conference on Online social networks
      October 2013
      254 pages
      ISBN:9781450320849
      DOI:10.1145/2512938

      Copyright © 2013 ACM

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      Publication History

      • Published: 7 October 2013

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      COSN '13 Paper Acceptance Rate22of138submissions,16%Overall Acceptance Rate69of307submissions,22%

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