Advertisement

Beyond Your Interests: Exploring the Information Behind User Tags

  • Weizhi Ma
  • Min Zhang
  • Yiqun Liu
  • Shaoping Ma
  • Lingfeng Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9362)

Abstract

Tags have been used in different social medias, such as Delicious, Flickr, LinkedIn and Weibo. In previous work, considerable efforts have been made to make use of tags without identification of their different types. In this study, we argue that tags in user profile indicate three different types of information, say the basics (age, status, locality, etc), interests and specialty of a person. Based on this novel user tag taxonomy, we propose a tag classification approach in Weibo to conduct a clearer image of user profiles, which makes use of three categories of features: general statistics feature (including user links with followers and followings), content feature and syntax feature. Furthermore, different from many previous studies on tag which concentrate on user specialties, such as expert finding, we find that valuable information can be discovered with the basics and interests user tags. We show some interesting findings in two scenarios, including user profiling with people come from different generations and area profiling with mass appeal, with large scale tag clustering and mining in over 6 million identical tags with 13 million users in Weibo data.

Keywords

Weibo Tag classification User group profiling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gupta, M., Li, R., Yin, Z., et al.: Survey on Social Tagging Techniques. ACM Sigkdd Explorations Newsletter 12(1), 58–72 (2010)CrossRefGoogle Scholar
  2. 2.
    Giannakidou, E., Koutsonikola, V., Vakali, A., et al.: Co-clustering tags and social data sources. In: The Ninth International Conference on Web Age Information Management, WAIM 2008, pp. 317–324 (2008)Google Scholar
  3. 3.
    Li, X., Snoek, C.G.M., Worring, M.: Unsupervised multi-feature tag relevance learning for social image retrieval. In: Conference on Image and Video Retrieval, pp. 10–17 (2010)Google Scholar
  4. 4.
    Xiao, J., Zhou, W., Tian, Q.: Exploring tag relevance for image tag re-ranking. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1069–1070. ACM (2012)Google Scholar
  5. 5.
    Zhu, X., Nejdl, W., Georgescu, M.: An adaptive teleportation random walk model for learning social tag relevance. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information retrieval, pp. 223–232. ACM (2014)Google Scholar
  6. 6.
    Giannakidou, E., Koutsonikola, V., Vakali, A., et al.: In & out zooming on time-aware user/tag clusters. Journal of Intelligent Information Systems 38(3), 685–708 (2012)CrossRefGoogle Scholar
  7. 7.
    Pennacchiotti, M., Popescu, A.M.: A Machine Learning Approach to Twitter User Classification. ICWSM 11, 281–288 (2011)Google Scholar
  8. 8.
    Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 531–538. ACM (2008)Google Scholar
  9. 9.
    Sigurbjörnsson, B., Zwol, R.V.: Flickr tag recommendation based on collective knowledge. In: Www 2008 Proc of International Conference on World Wide Web pp. 327–336 (2008)Google Scholar
  10. 10.
    Seitlinger, P., Kowald, D., Trattner, C., et al.: Recommending tags with a model of human categorization. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2381–2386. ACM (2013)Google Scholar
  11. 11.
    Ghosh, S., Sharma, N., Benevenuto, F., et al.: Cognos: crowdsourcing search for topic experts in microblogs. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 575–590. ACM (2012)Google Scholar
  12. 12.
    Liang, C., Liu, Z., Sun, M.: Expert finding for microblog misinformation identification. In: COLING (Posters), pp. 703–712 (2012)Google Scholar
  13. 13.
    Wang, X., Li, S., Zou, X., et al.: An automatic tag recommendation algorithm for micro-blogging users. In: 2013 International Conference on Computer Sciences and Applications (CSA), pp. 398–401. IEEE (2013)Google Scholar
  14. 14.
    Cunningham, H., Maynard, D., Bontcheva, K., et al.: GATE: an architecture for development of robust HLT applications. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, pp. 168–175 (2002)Google Scholar
  15. 15.
    Yu, K., Guan, G., Zhou, M.: Resume information extraction with cascaded hybrid model. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, pp. 499–506 (2005)Google Scholar
  16. 16.
    Tang, J., Yao, L., Zhang, D., et al.: A Combination Approach to Web User Profiling. ACM Transactions on Knowledge Discovery from Data 5(1), 293–302 (2010)CrossRefGoogle Scholar
  17. 17.
    Binlin, C., Jianming, F., Jingwei, H.: Detecting zombie followers in sina microblog based on the number of common friends. International Journal of Advancements in Computing Technology 5(2) (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Weizhi Ma
    • 1
  • Min Zhang
    • 1
  • Yiqun Liu
    • 1
  • Shaoping Ma
    • 1
  • Lingfeng Chen
    • 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