Dynamic User Attribute Discovery on Social Media

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9931)


Social media service defines a new paradigm of people communicating, self-expressing and sharing on the Web. Users in today’s social media platforms often post contents, inferring their interests/attributes, which are significant for many Web services such as social recommendation, personalized searching and online advertising. User attributes are temporally dynamic along with internal interest changing and external influence. Based on topic modeling, we present a probabilistic method for dynamic user attribute discovery. Our method automatically detects user attributes and models the dynamics using time windows and decay function, thereby facilitating more accurate recommendation. Evaluation on a Sina Weibo dataset shows the superiority in terms of precision, recall and F-measure as compared to baselines, such as static user attribute modeling.


Dynamic user attribute Topic model Time window 


  1. 1.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Semantic enrichment of Twitter posts for user profile construction on the social web. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 375–389. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Abel, F., Herder, E., Houben, G.J., Henze, N., Krause, D.: Cross-system user modeling and personalization on the social web. User Model. User-Adap. Inter. 23(2–3), 169–209 (2013)CrossRefGoogle Scholar
  3. 3.
    Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. pp. 27–34. AUAI Press (2009)Google Scholar
  4. 4.
    Bhattacharya, P., Zafar, M.B., Ganguly, N., Ghosh, S., Gummadi, K.P.: Inferring user interests in the Twitter social network. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 357–360. ACM (2014)Google Scholar
  5. 5.
    Bian, J., Yang, Y., Chua, T.S.: Multimedia summarization for trending topics in microblogs. In: Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management, pp. 1807–1812. ACM (2013)Google Scholar
  6. 6.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  7. 7.
    Chen, J., Wang, C., Wang, J.: A personalized interest-forgetting Markov model for recommendations. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)Google Scholar
  8. 8.
    Ding, Y., Jiang, J.: Extracting interest tags from Twitter user biographies. In: Jaafar, A., Mohamad Ali, N., Mohd Noah, S.A., Smeaton, A.F., Bruza, P., Bakar, Z.A., Jamil, N., Sembok, T.M.T. (eds.) AIRS 2014. LNCS, vol. 8870, pp. 268–279. Springer, Heidelberg (2014)Google Scholar
  9. 9.
    Gao, Q., Abel, F., Houben, G.J., Tao, K.: Interweaving trend and user modeling for personalized news recommendation. In: Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 100–103. IEEE Computer Society (2011)Google Scholar
  10. 10.
    Geng, X., Zhang, H., Song, Z., Yang, Y., Luan, H., Chua, T.S.: One of a kind: user profiling by social curation. In: Proceedings of the ACM International Conference on Multimedia, pp. 567–576. ACM (2014)Google Scholar
  11. 11.
    Lim, K.H., Datta, A.: Interest classification of Twitter users using Wikipedia. In: Proceedings of the 9th International Symposium on Open Collaboration, p. 22. ACM (2013)Google Scholar
  12. 12.
    Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on Twitter: a first look. In: Proceedings of the Fourth Workshop on Analytics for Noisy Unstructured Text Data, pp. 73–80. ACM (2010)Google Scholar
  13. 13.
    He, W., Liu, H., He, J., Tang, S., Du, X.: Extracting interest tags for non-famous users in social network. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 861–870. ACM (2015)Google Scholar
  14. 14.
    Ottoni, R., Las Casas, D.B., Pesce, J.P., Meira Jr., W., Wilson, C., Mislove, A., Almeida, V.: Of pins and tweets: investigating how users behave across image-and text-based social networks (2014)Google Scholar
  15. 15.
    Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pp. 487–494. AUAI Press (2004)Google Scholar
  16. 16.
    Sen, W., Xiaonan, Z., Yannan, D.: A collaborative filtering recommender system integrated with interest drift based on forgetting function. Int. J. u- and e- Serv. Sci. Technol. 8(4), 247–264 (2015)CrossRefGoogle Scholar
  17. 17.
    Wang, T., Liu, H., He, J., Du, X.: Mining user interests from information sharing behaviors in social media. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part II. LNCS, vol. 7819, pp. 85–98. Springer, Heidelberg (2013)Google Scholar
  18. 18.
    Xu, Z., Lu, R., Xiang, L., Yang, Q.: Discovering user interest on Twitter with a modified author-topic model. In: 2011 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 422–429. IEEE (2011)Google Scholar
  19. 19.
    Yan, X., Guo, J., Lan, Y., Cheng, X.: A biterm topic model for short texts. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1445–1456. International World Wide Web Conferences Steering Committee (2013)Google Scholar
  20. 20.
    Yin, H., Cui, B., Chen, L., Hu, Z., Huang, Z.: A temporal context-aware model for user behavior modeling in social media systems. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 1543–1554. ACM (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiu Huang
    • 1
  • Yang Yang
    • 1
  • Yue Hu
    • 1
  • Fumin Shen
    • 1
  • Jie Shao
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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