Advertisement

Examining the evolution of the Twitter elite network

  • Reza Motamedi
  • Soheil Jamshidi
  • Reza RejaieEmail author
  • Walter Willinger
Original Article

Abstract

The most-followed Twitter users and their pairwise relationships form a subgraph of Twitter users that we call the Twitter elite network. The connectivity patterns and information exchanges (in terms of replies and retweets) among these elite users illustrate how the “important” users connect and interact with one another on Twitter. At the same time, such an elite-focused view also provides valuable information about the structure of the Twitter network as a whole. This paper presents a detailed characterization of the structure and evolution of the top 10K Twitter elite network. We describe our technique for efficiently and accurately constructing the Twitter elite network along with social attributes of individual elite accounts and apply it to capture two snapshots of the top 10K elite network that are some 2.75 years apart. We show that a sufficiently large elite network is typically composed of 14–20 stable and cohesive communities that are recognizable in both snapshots, thus representing “socially meaningful” components of the elite network. We examine the changes in the identity and connectivity of individual elite users over time and characterize the community-level structure of the elite network in terms of bias in directed pairwise connectivity and relative reachability. We also show that both the reply and retweet activity between elite users are effectively contained within individual elite communities and are generally aligned with the centrality of the elite community users in both snapshots of the elite network. Finally, we observe that the majority of the regular Twitter users tend to have elite friends that belong to a single elite community. This finding offers a promising criterion for grouping regular users into “shadow partitions” based on their association with elite communities.

Notes

Acknowledgements

We would like to thank Hooman Mostafavi for his help in collecting the second snapshot of the Twitter elite network. This material is based upon work supported by the National Science Foundation under Grant IIS-0917381 and CNS-1320977.

References

  1. Al-Garadi M (2018) Analysis of online social network connections for identification of influential users: survey and open research issues. ACM Comput Surv 51:1–34CrossRefGoogle Scholar
  2. Avrachenkov K, Litvak N, Prokhorenkova LO, Suyargulova E (2014) Quick detection of high-degree entities in large directed networks. In: Proceedings of ICDM, IEEEGoogle Scholar
  3. Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of WSDM, ACMGoogle Scholar
  4. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:2008zbMATHGoogle Scholar
  5. Bollobás B (2013) Modern graph theory, vol 184. Springer, New YorkzbMATHGoogle Scholar
  6. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. In: Proceedings of WWW, ACMGoogle Scholar
  7. Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring user influence in Twitter: the million follower fallacy. In: ICWSMGoogle Scholar
  8. Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the Flickr social network. In: Proceedings of WWW, ACMGoogle Scholar
  9. Easley D, Kleinberg J (2010) Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  10. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174MathSciNetCrossRefGoogle Scholar
  11. Gonzalez R, Cuevas R, Motamedi R, Rejaie R, Cuevas A (2016) Assessing the evolution of google+ in its first two years. IEEE/ACM Trans Netw (ToN) 24(3):1813–1826CrossRefGoogle Scholar
  12. Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of WWW, ACMGoogle Scholar
  13. Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123MathSciNetCrossRefGoogle Scholar
  14. Motamedi R, Rejaie R, Lowd D, Willinger W, Gonzalez R (2014) Inferring coarse views of connectivity in very large graphs. In: Proceedings of COSNGoogle Scholar
  15. Motamedi R, Rezayi S, Rejaie R, Light R, Willinger W (2016) Characterizing twitter elite communities. In: Technical report CIS-2016–15, University of Oregon, www.cs.uoregon.edu/Reports/TR-2016-015.pdf
  16. Motamedi R, Rezayi S, Rejaie R, Willinger W (2018) On characterizing the twitter elite network. In: 2018 IEEE/ACM International conference on advances in social networks analysis and mining (ASONAM), pp 234–241Google Scholar
  17. Puranik T, Narayanan L (2017) Community detection in evolving networks. In: Proceedings of ASONAMGoogle Scholar
  18. Rejaie R, Torkjazi M, Valafar M, Willinger W (2010) Sizing up online social networks. IEEE Netw 24(5):32–37CrossRefGoogle Scholar
  19. Rosvall M, Bergstrom C (2007) Maps of information flow reveal community structure in complex networks. In: Proceedings of the national academy of sciencesGoogle Scholar
  20. Sobolevsky S, Campari R, Belyi A, Ratti C (2014) General optimization technique for high-quality community detection in complex networks. Phys Rev E 90(1):012811-1–012811-8CrossRefGoogle Scholar
  21. Stutzbach D, Rejaie R, Duffield N, Sen S, Willinger W (2009) On unbiased sampling for unstructured peer-to-peer networks. IEEE/ACM Trans Netw 17(2):377–390CrossRefGoogle Scholar
  22. Torkjazi M, Rejaie R, Willinger W (2009) Hot today, gone tomorrow: on the migration of myspace users. In: Proceedings of the ACM workshop on online social networks, pp 43–48Google Scholar
  23. Valafar M, Rejaie R, Willinger W (2009) Beyond friendship graphs: a study of user interactions in flickr. In: Proceedings of the ACM workshop on online social networks, pp 25–30Google Scholar
  24. Wikipedia. Sankey diagram. (2019), https://en.wikipedia.org/w/index.php?title=Sankey_diagram&oldid=740785912, Accessed: 2019 Apr 24
  25. Yang J, Leskovec J (2013) Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of WSDMGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Reza Motamedi
    • 1
  • Soheil Jamshidi
    • 1
  • Reza Rejaie
    • 1
    Email author
  • Walter Willinger
    • 2
  1. 1.University of OregonEugeneUSA
  2. 2.NIKSUN, Inc.PrincetonUSA

Personalised recommendations