Mining Interesting Topics in Twitter Communities

  • Eleni VathiEmail author
  • Georgios Siolas
  • Andreas Stafylopatis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9329)


We present a methodology for identifying user communities on Twitter, by defining a number of similarity metrics based on their shared content, following relationships and interactions. We then introduce a novel method based on latent Dirichlet allocation to extract user clusters discussing interesting local topics and propose a methodology to eliminate trivial topics. In order to evaluate the methodology, we experiment with a real-world dataset created using the Twitter Searching API.


Twitter Communities Topics 


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  1. 1.
    Ding, Y.: Community detection: Topological vs. topical. Journal of Informetrics 5(4), 498–514 (2011)CrossRefGoogle Scholar
  2. 2.
    Li, D., Ding, Y., Shuai, X., Bollen, J., Tang, J., Chen, S., Zhu, J., Rocha, G.: Adding community and dynamic to topic models. Journal of Informetrics 6(2), 237–253 (2012)CrossRefGoogle Scholar
  3. 3.
    Ruan, Y., Fuhry, D., Parthasarathy, S.: Efficient community detection in large networks using content and links. In: 22nd International Conference on World Wide Web, pp. 1089–1098 (2013)Google Scholar
  4. 4.
    Lim, K.H., Datta, A.: Tweets Beget Propinquity: Detecting Highly Interactive Communities on Twitter Using Tweeting Links. IEEE/WIC/ACM International Conference on Web Intelligence 1, 214–221 (2012)Google Scholar
  5. 5.
    Zhao, Z., Feng, S., Wang, Q., Huang, J.Z., Williams, G.J., Fan, J.: Topic oriented community detection through social objects and link analysis in social networks. Knowledge-Based Systems 26, 164–173 (2012)CrossRefGoogle Scholar
  6. 6.
    Goel, A., Sharma, A., Wang, D., Yin, Z.: Discovering similar users on twitter. In: 11th Workshop on Mining and Learning with Graphs, Chicago, Illinois (2013)Google Scholar
  7. 7.
    Zhang, Y., Wu, Y., Yang, Q.: Community Discovery in Twitter Based on User Interests. Journal of Computational Information Systems 8(3), 991–1000 (2012)Google Scholar
  8. 8.
    Xie, J., Kelley, S., Szymanski, B.K.: Overlapping Community Detection in Networks: The State-of-the-art and Comparative Study. ACM Computing Surveys 45(4), 1–35 (2013)CrossRefzbMATHGoogle Scholar
  9. 9.
    Yang, J., McAuley, J.J., Leskovec, J.: Community detection in networks with node attributes. In: IEEE 13th International Conference on Data Mining, pp. 1151–1156 (2013)Google Scholar
  10. 10.
    Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11), 613–620 (1975)CrossRefzbMATHGoogle Scholar
  11. 11.
    Frey, B.J., Dueck, D.: Clustering by Passing Messages Between Data Points. Science 315, 972–976 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Sander, J., Qin, X., Lu, Z., Niu, N., Kovarsky, A.: Automatic extraction of clusters from hierarchical clustering representations. 7th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. LNCS, vol. 2637, pp. 75–87. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  14. 14.
    Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Communications in Statistics-Simulation and Computation 3(1), 1–27 (1974)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Eleni Vathi
    • 1
    Email author
  • Georgios Siolas
    • 1
  • Andreas Stafylopatis
    • 1
  1. 1.Intelligent Systems Laboratory, School of Electrical and Computer EngineeringNational Technical University of AthensAthensGreece

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