Advertisement

On Recommending Hashtags in Twitter Networks

  • Su Mon Kywe
  • Tuan-Anh Hoang
  • Ee-Peng Lim
  • Feida Zhu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7710)

Abstract

Twitter network is currently overwhelmed by massive amount of tweets generated by its users. To effectively organize and search tweets, users have to depend on appropriate hashtags inserted into tweets. We begin our research on hashtags by first analyzing a Twitter dataset generated by more than 150,000 Singapore users over a three-month period. Among several interesting findings about hashtag usage by this user community, we have found a consistent and significant use of new hashtags on a daily basis. This suggests that most hashtags have very short life span. We further propose a novel hashtag recommendation method based on collaborative filtering and the method recommends hashtags found in the previous month’s data. Our method considers both user preferences and tweet content in selecting hashtags to be recommended. Our experiments show that our method yields better performance than recommendation based only on tweet content, even by considering the hashtags adopted by a small number (1 to 3)of users who share similar user preferences.

Keywords

Twitter hashtag recommendation systems 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Armentano, M.G., Godoy, D.L., Amandi, A.A.: Recommending information sources to information seekers in twitter. In: International Workshop on Social Web Mining (2011)Google Scholar
  2. 2.
    Armentano, M.G., Godoy, D.L.: A topology-based approach for followees recommendation in twitter. In: The 9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, ITWP 2011 (2011)Google Scholar
  3. 3.
    Chua, F.C.T., Lauw, H.W., Lim, E.-P.: Predicting item adoption using social correlation. In: SIAM Conference on Data Mining, pp. 367–378 (2011)Google Scholar
  4. 4.
    Correa, D., Sureka, A.: Mining tweets for tag recommendation on social media. In: The 3rd International Workshop on Search and Mining User-generated Contents (2011)Google Scholar
  5. 5.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)CrossRefGoogle Scholar
  6. 6.
    Huang, J., Thornton, K.M., Efthimiadis, E.N.: Conversational tagging in twitter. In: The 21st ACM Conference on Hypertext and hypermedia, pp. 173–178 (2010)Google Scholar
  7. 7.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: The 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis (2007)Google Scholar
  8. 8.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  9. 9.
    Lehmann, J., Gonçalves, B., Ramasco, J.J., Cattuto, C.: Dynamical classes of collective attention in twitter. In: The 21st International Conference on World Wide Web, pp. 251–260 (2012)Google Scholar
  10. 10.
    Li, T., Yu Wu, Y.Z.: Twitter hash tag prediction algorithm (2011), http://cerc.wvu.edu/download/WORLDCOMP%2711/2011%20CD%20papers/ICM3338.pdf
  11. 11.
    Mazzia, A., Juett, J.: Suggesting hashtags on twitter, http://www-personal.umich.edu/~amazzia/pubs/545-final.pdf
  12. 12.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35. Springer (2011)Google Scholar
  13. 13.
    Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Mining and Knowledge Discovery 5(1-2), 115–153 (2001)zbMATHCrossRefGoogle Scholar
  14. 14.
    Yang, L., Sun, T., Zhang, M., Mei, Q.: We know what @you #tag: does the dual role affect hashtag adoption? In: The 21st International Conference on World Wide Web, pp. 261–270 (2012)Google Scholar
  15. 15.
    Zangerle, E., Gassler, W.: Recommending #-tags in twitter. In: Proceedings of the CEUR Workshop (2011), http://ceur-ws.org/Vol-730/paper7.pdf

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Su Mon Kywe
    • 1
  • Tuan-Anh Hoang
    • 1
  • Ee-Peng Lim
    • 1
  • Feida Zhu
    • 1
  1. 1.Singapore Management UniversitySingapore

Personalised recommendations