Analyzing Tweet Cluster Using Standard Fuzzy C Means Clustering

  • Soumya Banerjee
  • Youakim Badr
  • Eiman Tamah Al-Shammari
Part of the Studies in Computational Intelligence book series (SCI, volume 526)


Since the inception of Web 2.0. the effort of socializing, interacting and referencing has been substantially enhanced.This is completely aided through the various means of social network expansions like blogging, public chat rooms and social networking sites such as Facebook, Twitter etc. Behavior on these websites leaves an electronic trail of social activity which can be analyzed and valuable information can be discerned. The development of such analysis has become phenomenal to foster psychological analysis, behavioral modeling and even commercializing the business activities under those paradigms itself. Therefore, micro-blogging service Tweeter recently has gained much interest to social network community with the trend of its Follower/Following Relationship, Mentions, trends, retweet, Twitter Lists etc. and the result of such impact could be realized while investigating diversified tweet clusters under the same community and under the same relevant discussion of topic. This chapter initiates a novel idea to analyze the random tweet cluster and its relevant trend through computational intelligence e.g. through Standard Fuzzy C Means clustering. The idea solicits and introduces a better method of clustering with more number of actually found dynamic clusters. Results have been evaluated with broader implication of analysis and research in futuristic Tweeter network.


Tweeter Cluster analysis Standard fuzzy C means algorithm Blogging C mean clustering 


  1. 1.
    Mislove, A., Gummadi, K.P., Druschel, P.: Exploiting social networks for Internet search. In: Proceedings of the 5th Workshop on Hot Topics in Networks (HotNets-V), Irvine, CA, November 2006Google Scholar
  2. 2.
    Breiger, R.L.: The analysis of social networks. In: Hardy, M., Nryman, A. (eds.) Handbook of Data Analysis, pp. 505–526. Sage Publications, London (2010)Google Scholar
  3. 3.
    Wu, S., Cornell University, USA, Jake, M., Hofman Yahoo! Research, NY, USA, Winter, A., Mason Yahoo! Research, NY, USA, Duncan, J., Watts Yahoo! Research, NY, USA, Who Says What to Whom on Twitter, 20th Annual World Wide Web Conference, ACM, Hyderabad, India (2011)Google Scholar
  4. 4.
    Lasswell, H.D.: The structure and function of communication in society. In: Bryson, L. (ed.) The Communication of Ideas, pp. 117–130. University of Illinois Press, Urbana (1948)Google Scholar
  5. 5.
    Walther, J.B., Carr, C.T., Choi, S.S.W., DeAndrea, D.C., Kim, J., Tong, S.T., Van Der Heide, B.: Interaction of interpersonal, peer, and media influence sources online. In: Papacharissi, Z. (ed.) A Networked Self: Identity, Community, and Culture on Social Network Sites, pp. 17–38. Routledge, New York (2010)Google Scholar
  6. 6.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Identifying influencers on twitter. In: Fourth ACM International Conference on Web Search and Data Mining (WSDM), Hong Kong (2011)Google Scholar
  7. 7.
    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, pp. 591–600. ACM, New York (2010)Google Scholar
  8. 8.
    Newman, M.E.J., Park, J.: Why social networks are different from other types of networks. Phys. Rev. E 68(3), 036122 (2003)CrossRefGoogle Scholar
  9. 9.
    Hannak, A. et al.: Tweetin’ in the rain: Exploring societal-scale effects on mood. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (2013)Google Scholar
  10. 10.
    Milgram, S.: The small world problem. Psychol. Today 2(1), 60–67 (1967)Google Scholar
  11. 11.
    Cha, M., Haddadi, H., Benevenuto, F., Gummad, K.P.: Measuring user influence on twitter: the million follower fallacy. In: 4th Int’l AAAI Conference on Weblogs and Social Media, Washington (2010)Google Scholar
  12. 12.
    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. ACM, New York (2010)Google Scholar
  13. 13.
    Jung, Y., Park, H., Du, D.-Z., Drake, B.L.: A decision criterion for the optimal number of clusters in hierarchical clustering. J. Bioinf. 18, S182–191 (2002)Google Scholar
  14. 14.
    Dunn, J.C.: Well separated clusters and optimal fuzzy partitions. J. Cybern. 4, 95–104 (1974)Google Scholar
  15. 15.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Machine Intell. 1(4), 224–227 (1979)Google Scholar
  16. 16.
    Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)Google Scholar
  17. 17.
    Bezdek, J.C. Bezdel’s fuzzy c-means clustering algorithm. Comput. Geosci. 10(2–3), 191–203 (1984)Google Scholar
  18. 18.
    Han, J., Kamber, M. : Data Mining Concepts and Techniques, 2nd edn. Elsevier, San Francisco (2006). ISBN 13: 978-1-55860-901-3 Google Scholar
  19. 19.
    Crane, R., Sornette, D.: Robust dynamic classes revealed by measuring the response function of a social system. Proc. Nat. Acad. Sci. 105(41), 15649–15653 (2008)Google Scholar
  20. 20. accessed 2nd July 2013
  21. 21.
    De Choudhury, M., Counts, S., Horvitz, E.: Major life changes and behavioral markers in social media: case of childbirth. In: Proceedings of the 16th ACM Conference on Computer Supported Cooperative Work (San Antonio, TX, USA, February 23–27, 2013). CSCW (2013)Google Scholar
  22. 22.
    Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces ACM, IUI, pp. 31–40, (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Soumya Banerjee
    • 1
  • Youakim Badr
    • 2
  • Eiman Tamah Al-Shammari
    • 3
  1. 1.Department of Computer ScienceBirla Institute of TechnologyMesraIndia
  2. 2.National Institute of Applied Sciences (INSA-Lyon)VilleurbanneFrance
  3. 3.College of Computing Science and Engineering, Kuwait UniversityKuwait CityKuwait

Personalised recommendations