Measuring and Visualizing Interest Similarity between Microblog Users

  • Jiayu Tang
  • Zhiyuan Liu
  • Maosong Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7923)


Microblog users share their life status and opinions via microposts, which usually reflect their interests. Measuring interest similarity between microblog users has thus received increasing attention from both academia and industry. In this paper, we design a novel framework for measuring and visualizing user interest similarity. The framework consists of four components: (1) Interest representation. We extract keywords from microposts to represent user interests. (2) Interest similarity computation. Based on the interest keywords, we design a ranking framework for measuring the interest similarity. (3) Interest similarity visualization. We propose a integrated word cloud scenario to provide a novel visual representation of user interest similarity. (4) Annotation data collection. We design an interactive game for microblog users to collect user annotations, which are used as training dataset for our similarity measuring method. We carry out experiments on Sina Weibo, the largest microblogging service in China, and get encouraging results.


interest similarity information visualization microblogging keyword extraction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Banerjee, N., Chakraborty, D., Dasgupta, K., Mittal, S., Joshi, A., Nagar, S., Rai, A., Madan, S.: User interests in social media sites: an exploration with micro-blogs. In: CIKM 2009, pp. 1823–1826. ACM, New York (2009)Google Scholar
  2. 2.
    Viegas, F.B., Wattenberg, M., Feinberg, J.: Participatory Visualization with Wordle. IEEE Transactions on Visualization and Computer Graphics 15, 1137–1144 (2009)CrossRefGoogle Scholar
  3. 3.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: WebKDD/SNA-KDD 2007, pp. 56–65. ACM, New York (2007)Google Scholar
  4. 4.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW 2010, pp. 591–600. ACM, New York (2010)Google Scholar
  5. 5.
    Wu, S., Hofman, J.M., Mason, W.A., Watts, D.J.: Who says what to whom on twitter. In: WWW 2011, pp. 705–714. ACM, New York (2011)Google Scholar
  6. 6.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on twitter. In: WSDM 2011, pp. 65–74. ACM, New York (2011)Google Scholar
  7. 7.
    Zhao, D., Rosson, M.B.: How and why people Twitter: the role that micro-blogging plays in informal communication at work. In: GROUP 2009, pp. 243–252. ACM, New York (2009)Google Scholar
  8. 8.
    Krishnamurthy, B., Gill, P., Arlitt, M.: A few chirps about twitter. In: 1st Workshop on Online Social Networks, pp. 19–24. ACM, New York (2008)CrossRefGoogle Scholar
  9. 9.
    Piao, S., Whittle, J.: A Feasibility Study on Extracting Twitter Users’ Interests Using NLP Tools for Serendipitous Connections. In: PASSAT/SocialCom 2011, pp. 910–915. IEEE CS Press, New Jersey (2011)Google Scholar
  10. 10.
    Wu, W., Zhang, B., Ostendorf, M.: Automatic generation of personalized annotation tags for Twitter users. In: HLT 2010, pp. 689–692. ACL, Stroudsburg (2010)Google Scholar
  11. 11.
    Yamaguchi, Y., Amagasa, T., Kitagawa, H.: Tag-based User Topic Discovery Using Twitter Lists. In: ASONAM 2011, pp. 13–20. IEEE CS Press, New Jersey (2011)Google Scholar
  12. 12.
    Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on twitter: a first look. In: AND 2010, pp. 73–80. ACM, New York (2010)Google Scholar
  13. 13.
    Paulovich, F.V., Toledo, F.M.B., Telles, G.P., Minghim, R., Nonato, L.G.: Semantic Wordification of Document Collections. Computer Graphics Forum 31, 1145–1153 (2012)CrossRefGoogle Scholar
  14. 14.
    Cui, W., Wu, Y., Liu, S., Wei, F., Zhou, M.X., Qu, H.: Context-Preserving, Dynamic Word Cloud Visualization. IEEE Computer Graphics and Applications 30, 42–53 (2010)CrossRefGoogle Scholar
  15. 15.
    Rivadeneira, A.W., Gruen, D.M., Muller, M.J., Millen, D.R.: Getting our head in the clouds: toward evaluation studies of tagclouds. In: CHI 2007, pp. 995–998. ACM, New York (2007)Google Scholar
  16. 16.
    Lohmann, S., Ziegler, J., Tetzlaff, L.: Comparison of Tag Cloud Layouts: Task-Related Performance and Visual Exploration. In: Gross, T., Gulliksen, J., Kotzé, P., Oestreicher, L., Palanque, P., Prates, R.O., Winckler, M. (eds.) INTERACT 2009, Part I. LNCS, vol. 5726, pp. 392–404. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Yu, L., Asur, S., Huberman, B.A.: What Trends in Chinese Social Media. arXiv:1107.3522v1 (2011)Google Scholar
  18. 18.
    A stacked model based on word lattice for Chinese word segmentation and part-of-speech tagging,
  19. 19.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: NAACL 2003, pp. 173–180. ACL, Stroudsburg (2003)Google Scholar
  20. 20.
    Liu, Z., Chen, X., Sun, M.: Mining the interests of Chinese microbloggers via keyword extraction. Frontiers of Computer Science in China 6, 76–87 (2012)MathSciNetGoogle Scholar
  21. 21.
    Joachims, T.: Optimizing search engines using clickthrough data. In: KDD 2002, pp. 133–142. ACM, New York (2002)Google Scholar
  22. 22.
    Halvey, M.J., Keane, M.T.: An assessment of tag presentation techniques. In: WWW 2007, pp. 1313–1314. ACM, New York (2007)Google Scholar
  23. 23.
    Chang, C., Lin, C.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 1–27 (2011)CrossRefGoogle Scholar
  24. 24.
    Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research 9, 1871–1874 (2008)zbMATHGoogle Scholar
  25. 25.
    Joachims, T.: Training linear SVMs in linear time. In: KDD 2006, pp. 217–226. ACM, New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jiayu Tang
    • 1
  • Zhiyuan Liu
    • 1
  • Maosong Sun
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

Personalised recommendations