Concept Similarity Based Academic Tweet Community Detection Using Label Propagation

  • G. Manju
  • T. V. Geetha
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8284)


In today’s world, Social Network plays a vital role in the society. Social Network users share their ideas, views, opinions, and develop their personal relationship. Social Network has major influence with academic community. This paper aims at detecting similar concept based academic tweets from the numerous available tweets and forming a community, considering the social relation between the tweeters. Academic community can support recommender system for researcher network. In our work, in order to extract concept similarity based academic community, concept similarity graph is constructed from twitter. Label Propagation algorithm is used to detect academic community. Normally, tweets contain user views, suggestions and discussion on a specific topic. In spite of tweets, containing other words in it, Concept words play a vital role in identifying about the aim of the tweeter in posting the tweet. Moreover, for academic topics, academic concepts are important. So, the Concepts are extracted and based on the similarity between concepts, academic community has been extracted from twitter. Label propagation has proven to be an effective method for detecting communities in complex networks. In this work, the new update rule based on social relation is introduced for Label propagation algorithm and used for concept based community detection. The experiment shows that, in comparison with standard label propagation algorithm, the label propagation with modified update rule reduces the number of iterations for convergence and as well was more effective in detecting communities.


Social Network Analysis Label Propagation Community detection Semi-supervised learning String Kernel Tweet Concept Similarity 


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  1. 1.
    Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: Proceedings of the First Workshop on Online Social Networks (WOSP 2008), pp. 19–24. ACM, New York (2008)CrossRefGoogle Scholar
  2. 2.
    Java, A., Song, X., Finin, T., Tseng, B.: 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, San Jose, California, August 12, pp. 56–65 (2007)Google Scholar
  3. 3.
    Honeycutt, C., Herring, S.C.: Beyond Microblogging: Conversation and Collaboration via Twitter. In: 42nd Hawaii International Conference on System Sciences HICSS, Big Island, HI, USA, pp. 1–10 (2009)Google Scholar
  4. 4.
    Antenucci, D., Handy, G., Modi, A., Tinkerhess, M.: Classification of Tweets via Clutering of Hashtags (2011),
  5. 5.
    Naaman, M., Boase, J., Lai, C.-H.: Is it Really About Me? Message Content in Social Awareness Streams. In: CSCW 2010, Savannah, Georgia, USA, pp. 255–256 (2010)Google Scholar
  6. 6.
    Ritter, A., Cherry, C., Dolan, B.: Unsupervised modeling of Twitter conversations, Human Language Technologies. In: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Los Angeles, California, pp. 172–180 (2010)Google Scholar
  7. 7.
    Phuvipadawat, S., Murata, T.: Detecting a Multi-Level Content Similarity from Microblogs based on Community Structures and Named Entities. Journal of Emerging Technologies in Web Intelligence 3(1), 11–19 (2011)CrossRefGoogle Scholar
  8. 8.
    Rosa, K.D., Shah, R., Lin, B., Gershman, A., Frederking, R.: Topical clustering of tweets. In: Proceedings of SIGIR Workshop on Social Web Search and Mining (2011)Google Scholar
  9. 9.
    Luiten, M., Kosters, W.A., Takes, F.W.: Topical Influence on Twitter: A Feature Construction Approach. In: Proceedings of 24th Benelux Conference on Artificial Intelligence(BNAIC 2012), pp. 139–146 (2012)Google Scholar
  10. 10.
    Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)Google Scholar
  11. 11.
    Zhu, X., Ghahraman, Z.: Learning from Labeled and Unlabeled Data with Label Propagation.Tech report CMU-CALD-02-107 (2002)Google Scholar
  12. 12.
    Šubelj, L., Bajec, M.: Unfolding network communities by combining defensive and offensive label propagation. In: Proceedings of the ECML PKDD Workshop on the Analysis of Complex Networks (ACNE 2010), Barcelona, Spain, pp. 87–104 (2010)Google Scholar
  13. 13.
    Xie, J., Szymanski, B.K.: Community detection using a neighborhood strength driven label propagation algorithm. In: IEEE NSW 2011, West Point, NY, pp. 188–195 (2011)Google Scholar
  14. 14.
  15. 15.
    Twitter Streaming API,
  16. 16.
    Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification using string kernels. Journal of Machine Learning Research 2, 419–444 (2002)MATHGoogle Scholar
  17. 17.
    Martins, Mario A. T. Figueiredo, Pedro M. Q. Aguiar.: Kernels and similarity measures for text classification, In: 6th Conference on telecommunications—ConfTele 2007, Peniche, Portugal(2007) Google Scholar
  18. 18.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)Google Scholar
  19. 19.
    Cordasco, G., Gargano, L.: Community Detection via Semi-Synchronous Label Propagation Algorithms, arxiv:1103.4550Google Scholar
  20. 20.
    Wang, F., Zhang, C.: Label Propagation through Linear Neighbourhoods. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 85–992. ACM New York (2006)Google Scholar
  21. 21.
    Chen, J., Ji, D., Tan, C.L., Niu, Z.: Relation extraction using label propagation based semi-supervised learning. In: ACL-44th Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Sydney, pp. 129–136 (2006)Google Scholar
  22. 22.
    Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 98(2), 404–409 (2001)CrossRefMATHMathSciNetGoogle Scholar
  23. 23.
    Fell, D.A., Wagner, A.: The small world of metabolism. Nature Biotechnology 18(11), 1121–1122 (2000)CrossRefGoogle Scholar
  24. 24.
    Danon, L., Duch, J., Arenas, A., Diaz-guilera, A.: Comparing community structure identification. Journal of Statistical Mechanics:Theory and Experiment 9008, 09008 (2005)Google Scholar
  25. 25.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA 99(12), 7821–7826 (2002)CrossRefMATHMathSciNetGoogle Scholar
  26. 26.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)Google Scholar
  27. 27.
    Barber, M.J.: Detecting network communities by propagating labels under constraints. Phys. Rev. E 80, 026129 (2009)Google Scholar
  28. 28.
    Leung, I.X.Y., Hui, P., Li, P., Crowcroft, J.: Towards real-time community detection in large networks. Phys. Rev. E 79, 066107 (2009)Google Scholar
  29. 29.
    Jia, G., Cai, Z., Musolesi, M., Wang, Y., Tennant, D.A., Weber, R.J.M., Heath, J.K., He, S.: Community Detection in Social and Biological Networks Using Differential Evolution. In: Hamadi, Y., Schoenauer, M. (eds.) LION 2012. LNCS, vol. 7219, pp. 71–85. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  30. 30.
    Bansal, S., Bhowmick, S., Paymal, P.: Fast community detection for dynamic complex networks. In: Mangioni, G. (ed.) CompleNet 2010. CCIS, vol. 116, pp. 196–207. Springer, Heidelberg (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • G. Manju
    • 1
  • T. V. Geetha
    • 1
  1. 1.Anna UniversityChennaiIndia

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