How Influential Are You: Detecting Influential Bloggers in a Blogging Community

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


Blogging is a popular activity with high impact on marketing, shaping public opinions, and informing the world about major events from a grassroots point of view. Influential bloggers are recognized by businesses as significant forces for product promotion or demotion, and by oppressive political regimes as serious threats to their power. This paper studies the problem of identifying influential bloggers in a blogging community, BlogCatalog, by using network centrality metrics. Our analysis shows that bloggers are connected in a core-periphery network structure, with the highly influential bloggers well connected with each others forming the core, and the non-influential bloggers at the periphery. The six node centrality metrics we analyzed are highly correlated, showing that an aggregate centrality score as a measure of influence will be stable to variations in centrality metrics.


social networks influence network centrality blogosphere 


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  1. 1.
    Gillmor, D.: We the Media: Grassroots Journalism by the People, for the People. O’Reilly (2006)Google Scholar
  2. 2.
    Rao, L.: Wordpress now powers 22 percent of new active websites in the u.s (2011),
  3. 3.
    Richardson, M., Domingos, P.: Mining knowledge-sharing sites for viral marketing. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 61–70 (2002)Google Scholar
  4. 4.
    Gruhl, D., Guha, R., Kumar, R., Novak, J., Tomkins, A.: The predictive power of online chatter. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 78–87 (2005)Google Scholar
  5. 5.
    Mishne, G., de Rijke, M.: Deriving wishlists from blogs show us your blog, and we’ll tell you what books to buy. In: Proceedings of the 15th International Conference on World Wide Web, pp. 925–926 (2006)Google Scholar
  6. 6.
    Scoble, R., Israel, S.: Naked conversations: how blogs are changing the way businesses talk with customers. John Wiley (2006)Google Scholar
  7. 7.
    Coffman, T., Marcus, S.: Dynamic classification of groups through social network analysis and hmms. In: Proceedings of IEEE Aerospace Conference, pp. 3197–3205 (2004)Google Scholar
  8. 8.
    Technorati: State of the blogosphere 2011: Introduction and methodology (2011),
  9. 9.
    Keller, E., Berry, J.: One American in ten tells the other nine how to vote, where to eat and, what to buy. They are The Influentials. The Free Press (2003)Google Scholar
  10. 10.
    Aubrey, A.: Mcdonald’s courts mom bloggers when changing the menu (2011),
  11. 11.
  12. 12.
    Aral, S., Muchnika, L., Sundararajana, A.: Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. PNAS 106, 21544–21549 (2009)CrossRefGoogle Scholar
  13. 13.
    Aral, S., Walker, D.: Identifying influential and susceptible members of social networks. Science 337, 337–341 (2012)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Freeman, L.: A set of measures of centrality based upon betweenness. Sociometry 40, 35–41 (1977)CrossRefGoogle Scholar
  15. 15.
    Newman, M.E.J.: Networks: An Introduction. Oxford University Press (2010)Google Scholar
  16. 16.
    Brandes, U.: On variants of shortest-path betweenness centrality and their generic computation. Social Networks 30, 136–145 (2008)CrossRefGoogle Scholar
  17. 17.
    Estrada, E., Rodriguez-Velazquez, J.A.: Subgraph centrality in complex networks. Physical Review E 71, 056103 (2005)Google Scholar
  18. 18.
    Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology 2, 113–120 (1972)CrossRefGoogle Scholar
  19. 19.
    Zafarani, R., Liu, H.: Social computing data repository at ASU (2009),
  20. 20.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42 (2007)Google Scholar
  21. 21.
    Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., Wiener, J.: Graph structure in the web. In: Proceedings of the 9th International World Wide Web Conference, pp. 309–320 (2000)Google Scholar
  22. 22.
    Watts, D.J., Strogatz, S.: Collective dynamics of ‘small-world’ networks. Nature 393, 440–442 (1998)CrossRefGoogle Scholar
  23. 23.
    Barabasi, A.L.: Linked: How Everything Is Connected to Everything Else and What It Means. Plume (2003)Google Scholar
  24. 24.
    Valente, T., Coronges, K., Lakon, C., Costenbader, E.: How correlated are network centrality measures? Connections 28, 16–26 (2008)Google Scholar
  25. 25.
    Manski, C.: Identification of endogenous social effects: The reflection problem. The Review of Economic Studies 60, 531–542 (1993)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Aral, S., Alstyne, M.V.: The diversity-bandwidth trade-off. American Journal of Sociology 117, 90–171 (2011)CrossRefGoogle Scholar
  27. 27.
    Evans, D.: Beyond Influencers: Social Network Properties and Viral Marketing. Psychster Inc. (2009)Google Scholar
  28. 28.
    Snijders, T., van de Bunt, G., Steglich, C.: Introduction to actor-based models for network dynamics. Social Networks 32, 44–60 (2010)CrossRefGoogle Scholar
  29. 29.
    Bramoull, Y., Djebbari, H., Fortin, B.: Identification of peer effects through social networks. Journal of Econometrics 150, 41–55 (2009)MathSciNetCrossRefGoogle Scholar
  30. 30.
    Sacerdote, B.: Peer effects with random assignment: Results for dartmouth roommates. The Quarterly Journal of Economics 116, 681–704 (2001)zbMATHCrossRefGoogle Scholar
  31. 31.
    Christakis, N., Fowler, J.: The spread of obesity in a large social network over 32 years. The New England Journal of Medicine 357, 370–379 (2007)CrossRefGoogle Scholar
  32. 32.
    Agarwal, N., Liu, H., Tang, L., Yu, P.S.: Identifying the influential bloggers in a community. In: Proceedings of the International Conference on Web Search and Web Data Mining, pp. 207–218 (2008)Google Scholar
  33. 33.
    Trusov, M., Bodapati, A., Bucklin, R.: Determining influential users in internet social networks. Journal of Marketing Research 47, 643–658 (2010)CrossRefGoogle Scholar
  34. 34.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 261–270 (2010)Google Scholar
  35. 35.
    Tang, X., Yang, C.: Identifying influential users in an online healthcare social network. In: Proceedings of the IEEE International Conference on Intelligence and Security Informatics, pp. 43–48 (2010)Google Scholar
  36. 36.
    Han, B., Srinivasan, A.: Your friends have more friends than you do: identifying influential mobile users through random walks. In: Proceedings of the Thirteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 5–14 (2012)Google Scholar
  37. 37.
    Ilyas, M.U., Radha, H.: Identifying influential nodes in online social networks using principal component centrality. In: Proceedings of IEEE International Conference on Communications, pp. 1–5 (2011)Google Scholar
  38. 38.
    Subbian, K., Melville, P.: Supervised rank aggregation for predicting influencers in twitter. In: SocialCom, pp. 661–665 (2011)Google Scholar
  39. 39.
    Ghosh, R., Lerman, K.: Predicting influential users in online social networks. In: Proceedings of KDD Workshop on Social Network Analysis (2010)Google Scholar
  40. 40.
    Shetty, J., Adibi, J.: Discovering important nodes through graph entropy the case of enron email database. In: Proceedings of the 3rd International Workshop on Link Discovery, pp. 74–81 (2005)Google Scholar
  41. 41.
    Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the 16th International Conference on World Wide Web, pp. 221–230 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Computer Science & EngineeringUniversity of South FloridaTampaUSA
  2. 2.Department of SociologyUniversity of South FloridaTampaUSA

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