An Analysis of the Overlap of Categories in a Network of Blogs

  • Priya Saha
  • Ronaldo Menezes
Part of the Studies in Computational Intelligence book series (SCI, volume 476)


We live in a world where information flows very rapidly and people become aware of events on the other side of the world in a matter of seconds; this a consequence of the globalized, fully-connected world we live in. Information spreads via many different channels, but more recently we have witnessed the birth of the information-over-online-social-network phenomena. This means that more and more people get their news from online social networks such Facebook and microblogs such as Twitter. Yet, another source of information are weblogs (or blogs). Bloggers (people who write to blog or own a blog) are capable of influencing a lot of people and they even tend to be sources of information to mainstream news media. This paper delves into an issue relating to the ability of information to spread, but instead of tracking information itself, we look at the infrastructure that is in place linking blogs.We argue that the structure itself is an enabler or disabler of information spread depending on a categorization. This paper categorizes blogs and studies the level of overlap between these categories.


Online Social Network Giant Component Full Network Topic Cluster Natural Language Processing Technique 
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.


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  1. 1.
    Amaral, L.A., Scala, A., Barthelemy, M., Stanley, H.E.: Classes of small-world networks. Proc. Natl. Acad. Sci. 97(21), 11149–11152 (2000)CrossRefGoogle Scholar
  2. 2.
    Barabási, A.L., Albert, R.: Emergence of scaling in random networks. Science 286(5439), 509 (1999)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bastian, M., Heymann, S., Jacomy, M.: Gephi: An open source software for exploring and manipulating networks. In: International AAAI Conference on Weblogs and Social Media (2009)Google Scholar
  4. 4.
    Brown, J., Broderick, A.J., Lee, N.: Word of mouth communication within online communities: Conceptualizing the online social network. Journal of Interactive Marketing 21(3), 2–20 (2007)CrossRefGoogle Scholar
  5. 5.
    Centola, D.: The spread of behavior in an online social network experiment. Science 329(5996), 1194–1197 (2010)CrossRefGoogle Scholar
  6. 6.
    Cha, M., Mislove, A., Gummadi, K.P.: A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 721–730. ACM (2009)Google Scholar
  7. 7.
    Clark, G.: A Farewell to Alms: A Brief Economic History of the World. Princeton University Press (2008)Google Scholar
  8. 8.
    Cointet, J.-P., Roth, C.: Socio-semantic dynamics in a blog network. In: Proceedings of the 2009 International Conference on Computational Science and Engineering, CSE 2009, vol. 04, pp. 114–121. IEEE Computer Society, Washington, DC (2009)CrossRefGoogle Scholar
  9. 9.
    Devezas, J.L., Ribeiro, C., Nunes, S.: Studying blog features over link popularity. In: Proceedings of the First Workshop on Social Media Analytics, SOMA 2010, pp. 31–34. ACM, New York (2010)CrossRefGoogle Scholar
  10. 10.
    Dorogovtsev, S.N., Goltsev, A.V., Mendes, J.F.F.: Pseudofractal scale-free web. Phys. Rev. E 65(6), 066122 (2002)Google Scholar
  11. 11.
    Heer, J., Boyd, D.: Vizster: Visualizing online social networks. In: Proceedings of the 2005 IEEE Symposium on Information Visualization, INFOVIS 2005, pp. 32–39. IEEE Computer Society (2005)Google Scholar
  12. 12.
    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 (2010)Google Scholar
  13. 13.
    Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 497–506. ACM (2009)Google Scholar
  14. 14.
    Newman, M.: The structure and function of complex networks. SIAM Review 45(2), 167–256 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Qureshi, M.A., Younus, A., Saeed, M., Touheed, N., Pianta, E., Tymoshenko, K.: Identifying and ranking topic clusters in the blogosphere. In: Proceedings of the 2nd Workshop on The People’s Web Meets NLP: Collaboratively Constructed Semantic Resources, Beijing, China, pp. 55–62 (August 2010)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.BioComplex Laboratory, Department of Computer SciencesFlorida Institute of TechnologyMelbourneUSA

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