A community role approach to assess social capitalists visibility in the Twitter network

  • Nicolas DuguéEmail author
  • Vincent Labatut
  • Anthony Perez
Original Article


In the context of Twitter, social capitalists are specific users trying to increase their number of followers and interactions by any means. These users are not healthy for the service, because they are either spammers or real users flawing the notions of influence and visibility. Studying their behavior and understanding their position in Twitter is thus of important interest. It is also necessary to analyze how these methods effectively affect user visibility. Based on a recently proposed method allowing to identify social capitalists, we tackle both points by studying how they are organized, and how their links spread across the Twitter follower–followee network. To that aim, we consider their position in the network w.r.t. its community structure. We use the concept of community role of a node, which describes its position in a network depending on its connectivity at the community level. However, the topological measures originally defined to characterize these roles consider only certain aspects of the community-related connectivity, and rely on a set of empirically fixed thresholds. We first show the limitations of these measures, before extending and generalizing them. Moreover, we use an unsupervised approach to identify the roles, in order to provide more flexibility relatively to the studied system. We then apply our method to the case of social capitalists and show they are highly visible on Twitter, due to the specific roles they hold.


Twitter Social network Social capitalism Influence Community roles 


  1. Arora S, Ge R, Sachdeva S, Schoenebeck G (2012) Finding overlapping communities in social networks: toward a rigorous approach. In EC’12Google Scholar
  2. Blondel V, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech 10:P10008CrossRefGoogle Scholar
  3. Bosker B (2011) Twitter: we now have over 200 million accountsGoogle Scholar
  4. Burt RS (1990) Detecting role equivalence. Soc Netw 12(1):83–97MathSciNetCrossRefGoogle Scholar
  5. Cha M, Haddadi H, Benevenuto F, Gummadi PK (2010) Measuring User Influence in Twitter: The Million Follower Fallacy. In ICWSM ’10: Proc. of int. AAAI Conference on Weblogs and Social, 2010Google Scholar
  6. Davies D, Bouldin D (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227CrossRefGoogle Scholar
  7. Dugué N, Perez A (2014) Social capitalists on Twitter: detection, evolution and behavioral analysis. Soc Netw Anal Min 4(1):1–15CrossRefGoogle Scholar
  8. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3–5):75–174MathSciNetCrossRefGoogle Scholar
  9. Ghosh S, Viswanath B, Kooti F, Sharma N, Korlam G, Benevenuto F, Ganguly N, Gummadi K (2012) Understanding and combating link farming in the twitter social network. In WWW, pp 61–70Google Scholar
  10. Guimerà R, Amaral L (2005) Functional cartography of complex metabolic networks. Nature 433:895–900CrossRefGoogle Scholar
  11. Holland PW, Laskey KB, Leinhardt S (1983) Stochastic blockmodels: first steps. Soc Netw 5(2):109–137MathSciNetCrossRefGoogle Scholar
  12. Java A, Song X, Finin T, Tseng B (2007) Why we twitter: understanding microblogging usage and communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, WebKDD/SNA-KDD ’07, pp 56–65Google Scholar
  13. Klimm F, Borge-Holthoefer J, Wessel N, Kurths J, Zamora-Lpez G (2014) Individual nodes contribution to the mesoscale of complex networks. New J Phys 16(12):125006CrossRefGoogle Scholar
  14. Labatut V, Dugué N, Perez A (2014) Identifying the community roles of social capitalists in the twitter network. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on, IEEE, pp 371–374Google Scholar
  15. Lancichinetti A, Kivelä M, Saramäki J, Fortunato S (2010) Characterizing the community structure of complex networks. PLoS One 5(8):e11976CrossRefGoogle Scholar
  16. Lee K, Caverlee J, Webb S (2010) Uncovering social spammers: social honeypots \(+\) machine learning. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’10, ACM, New York, NY, USA, pp 435–442Google Scholar
  17. Lee K, Eoff B, Caverlee J (2011) Seven months with the devils: a long-term study of content polluters on twitter. In International AAAI Conference on Weblogs and Social MediaGoogle Scholar
  18. Leicht EA, Newman MEJ (2008) Community structure in directed networks. Phys Rev Lett 100(11):118703CrossRefGoogle Scholar
  19. Liao W-K (2009) Parallel k-means data clustering. Northwestern University, Electrical Engineering and Computer Science Department.
  20. Lorrain F, White HC (1971) Structural equivalence of individuals in social networks. J Math Sociol 1(1):49–80CrossRefGoogle Scholar
  21. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113CrossRefGoogle Scholar
  22. Rodgers S (2013) Behind the numbers: how to understand big moments on Twitter. Twitter.
  23. Scripps J, Tan P-N, Esfahanian A-H (2007) Node roles and community structure in networks. In WebKDD/SNAKDD, pp 26–35Google Scholar
  24. Simpson GG (1943) Mammals and the nature of continents. Am J Sci 241:1–41CrossRefGoogle Scholar
  25. Suh B, Hong L, Pirolli P, Chi EH (2010) Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In SCA’10, pp 177–184Google Scholar

Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Nicolas Dugué
    • 1
    Email author
  • Vincent Labatut
    • 2
  • Anthony Perez
    • 1
  1. 1.Université d’Orléans, LIFO EA 4022OrléansFrance
  2. 2.Université d’Avignon, LIA EA 4128AvignonFrance

Personalised recommendations