Dynamic Targeting in an Online Social Medium

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7710)


Online human interactions take place within a dynamic hierarchy, where social influence is determined by qualities such as status, eloquence, trustworthiness, authority and persuasiveness. In this work, we consider topic-based Twitter interaction networks, and address the task of identifying influential players. Our motivation is the strong desire of many commerical entities to increase their social media presence by engaging positively with pivotal bloggers and Tweeters. After discussing some of the issues involved in extracting useful interaction data from a Twitter feed, we define the concept of an active node subnetwork sequence. This provides a time-dependent, topic-based, summary of relevant Twitter activity. For these types of transient interactions, it has been argued that the flow of information, and hence the influence of a node, is highly dependent on the timing of the links. Some nodes with relatively small bandwidth may turn out to be key players because of their prescience and their ability to instigate follow-on network activity. To simulate a commercial application, we build an active node subnetwork sequence based on key words in the area of travel and holidays. We then compare a range of network centrality measures, including a recently proposed version that accounts for the arrow of time, with respect to their ability to rank important nodes in this dynamic setting. The centrality rankings use only connectivity information (who Tweeted whom, when), but if we post-process the results by examining account details, we find that the time-respecting, dynamic, approach, which looks at the follow-on flow of information, is less likely to be ‘misled’ by accounts that appear to generate large numbers of automatic Tweets with the aim of pushing out web links. We then benchmark these algorithmically derived rankings against independent feedback from five social media experts who judge Twitter accounts as part of their professional duties. We find that the dynamic centrality measures add value to the expert view, and indeed can be hard to distinguish from an expert in terms of who they place in the top ten. We also highlight areas where the algorithmic approach can be refined and improved.


Centrality Measure Active Node Twitter User Twitter Data Centrality Ranking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Newman, M.E.J.: Networks an Introduction. Oxford Univerity Press, Oxford (2010)zbMATHGoogle Scholar
  2. 2.
    Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Nat. Acad. Sci. 106(36), 15274–15278 (2009)CrossRefGoogle Scholar
  3. 3.
    Grindrod, P., Higham, D.J., Parsons, M.C., Estrada, E.: Communicability across evolving networks. Physical Review E 83, 046120 (2011)CrossRefGoogle Scholar
  4. 4.
    Barabási, A.L.: The origin of bursts and heavy tails in human dynamics. Nature 435, 207–211 (2005)CrossRefGoogle Scholar
  5. 5.
    Tang, J., Scellato, S., Musolesi, M., Mascolo, C., Latora, V.: Small-world behavior in time-varying graphs. Phys. Rev. E 81, 05510 (2010)Google Scholar
  6. 6.
    Isella, L., Romano, M., Barrat, A., Cattuto, C., Colizza, V., Van den Broeck, W., Gesualdo, F., Pandolfi, E., Rav, L., Rizzo, C., Tozzi, A.E.: Close encounters in a pediatric ward: Measuring face-to-face proximity and mixing patterns with wearable sensors. PLoS ONE 6(2), e17144 (2011)Google Scholar
  7. 7.
    Bajardi, P., Barrat, A., Natale, F., Savini, L., Colizza, V.: Dynamical patterns of cattle trade movements. PLoS ONE 6(5), e19869 (2011)Google Scholar
  8. 8.
    Mucha, P.J., Richardson, T., Macon, K., Porter, M.A., Onnela, J.P.: Community structure in time-dependent, multiscale, and multiplex networks. Science 328, 876–878 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Bassett, D.S., Wymbs, N.F., Porter, M.A., Mucha, P.J., Carlson, J.M., Grafton, S.T.: Dynamic reconfiguration of human brain networks during learning. Proc. Nat. Acad. Sci. 108 (2011), doi: 10.1073/pnas.1018985108Google Scholar
  10. 10.
    Grindrod, P., Higham, D.J.: Evolving graphs: Dynamical models, inverse problems and propagation. Proc. Roy. Soc. A 466, 753–770 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Gleave, E., Welser, H.T., Lento, T.M., Smith, M.A.: A conceptual and operational definition of ‘social role’ in online community. In: Proceedings of the 42nd Hawaii International Conference on System Sciences, pp. 1–11. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  12. 12.
    Huffaker, D.: Dimensions of leadership and social influence in online communities. Human Communication Research 36, 593–617 (2010)CrossRefGoogle Scholar
  13. 13.
    Mantzaris, A.V., Higham, D.J.: A model for dynamic communicators. European Journal of Applied Mathematics (to appear, 2012)Google Scholar
  14. 14.
    Bonchi, F., Castillo, C., Gionis, A., Jaimes, A.: Social network analysis and mining for business applications. ACM Trans. Intell. Syst. Technol. 2(3), 22:1–22:37 (2011)CrossRefGoogle Scholar
  15. 15.
    Shamma, D.A., Kennedy, L., Churchill, E.F.: In the limelight over time: Temporalities of network centrality. In: Proceedings of the 29th International Conference on Human Factors in Computing Systems, CSCW 2011, ACM (2011)Google Scholar
  16. 16.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 65–74. ACM, New York (2011)Google Scholar
  17. 17.
    Lerman, K., Ghosh, R., Surachawala, T.: Social contagion: An empirical study of information spread on digg and twitter follower graphs. CoRR abs/1202.3162 (2012)Google Scholar
  18. 18.
    Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K.P.: Measuring user influence in Twitter: The million follower fallacy. In: ICWSM 2010: Proceedings of International AAAI Conference on Weblogs and Social (2010)Google Scholar
  19. 19.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 591–600. ACM, New York (2010)Google Scholar
  20. 20.
    Estrada, E.: The Structure of Complex Networks. Oxford University Press, Oxford (2011)Google Scholar
  21. 21.
    Newman, M.: A measure of betweenness centrality based on random walks. Social Networks 27(1), 39–54 (2005)CrossRefGoogle Scholar
  22. 22.
    Borgatti, S.P.: Centrality and network flow. Social Networks 27, 55–71 (2005)CrossRefGoogle Scholar
  23. 23.
    Katz, L.: A new index derived from sociometric data analysis. Psychometrika 18, 39–43 (1953)zbMATHCrossRefGoogle Scholar
  24. 24.
    Holme, P.: Network reachability of real-world contact sequences. Physical Review E 71(4), 046119 (2005)CrossRefGoogle Scholar
  25. 25.
    Kim, H., Tang, J., Anderson, R., Mascolo, C.: Centrality prediction in dynamic human contact networks. Comput. Netw. 56(3), 983–996 (2012)CrossRefGoogle Scholar
  26. 26.
    Kossinets, G., Kleinberg, J., Watts, D.: The structure of information pathways in a social communication network. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Datamining, KDD 2008, pp. 435–443. ACM, New York (2008)Google Scholar
  27. 27.
    Grindrod, P., Higham, D.J.: A matrix iteration for dynamic network summaries. SIAM Review (to appear, 2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Bloom Agency: Green Sand FoundryUnited Kingdom
  2. 2.Department of Mathematics and StatisticsUniversity of StrathclydeUnited Kingdom
  3. 3.Department of MathematicsUniversity of ReadingReadingUnited Kingdom

Personalised recommendations