Incorporating User Grouping into Retweeting Behavior Modeling

  • Jinhai Zhu
  • Shuai Ma
  • Hui Zhang
  • Chunming Hu
  • Xiong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


The variety among massive users makes it difficult to model their retweeting activities. Obviously, it is not suitable to cover the overall users by a single model. Meanwhile, building one model per user is not practical. To this end, this paper presents a novel solution, of which the principle is to model the retweeting behavior over user groups. Our system, GruBa, consists of three key components for extracting user based features, clustering users into groups, and modeling upon each group. Particularly, we look into the user interest from different perspectives including long-term/short-term interests and explicit/implicit interests. We have evaluated the performance of GruBa using datasets of real-world social networking applications, showcasing its benefits.


User grouping Social networks Behavior modeling 



Ma is supported in part by NSFC U1636210, 973 Program 2014CB340300, NSFC 61421003, and MSRA Collaborative Research Program. Li is supported in part by NSFC U1636123 & 61403090. For any correspondence, please refer to Shuai Ma.


  1. 1.
  2. 2.
    Bhattacharya, P., et al.: Inferring user interests in the Twitter social network. In: RecSys (2014)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. JMLR 3, 993–1022 (2003)zbMATHGoogle Scholar
  4. 4.
    Ciot, M., Sonderegger, M., Ruths, D.: Gender inference of Twitter users in Non-English contexts. In: EMNLP (2013)Google Scholar
  5. 5.
    Devineni, P., Koutra, D., Faloutsos, M., Faloutsos, C.: Facebook wall posts: a model of user behaviors. Soc. Netw. Anal. Min. 7(1), 6:1–6:15 (2017)CrossRefGoogle Scholar
  6. 6.
    Duan, L., Ma, S., Aggarwal, C.C., Ma, T., Huai, J.: An ensemble approach to link prediction. IEEE Trans. Knowl. Data Eng. 29(11), 2402–2416 (2017)CrossRefGoogle Scholar
  7. 7.
    Ranganath, S., et al.: Predicting online protest participation of social media users. CoRR, abs/1512.02968 (2015)Google Scholar
  8. 8.
    Everitt, B.: Cluster Analysis. Heinemann Educational Books Ltd., Portsmouth (1974)zbMATHGoogle Scholar
  9. 9.
    Fan, Y., Chen, Y., Tung, K., Wu, K., Chen, A.L.P.: A framework for enabling user preference profiling through Wi-Fi logs. In: ICDE (2016)Google Scholar
  10. 10.
    Feng, W., et al.: Retweet or not?: personalized Tweet re-ranking. In: WSDM (2013)Google Scholar
  11. 11.
    Feng, W., Wang, J.: We can learn your #hashtags: connecting Tweets to explicit topics. In: ICDE (2014)Google Scholar
  12. 12.
    Giatsoglou, M., Chatzakou, D., Shah, N., Faloutsos, C., Vakali, A.: Retweeting activity on Twitter: signs of deception. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9077, pp. 122–134. Springer, Cham (2015). Scholar
  13. 13.
    Guo, Z., et al.: Characterizing user behavior in weibo. In: MUSIC (2012)Google Scholar
  14. 14.
    He, C., Ma, H., Kang, S., Cui, R.: An overlapping community detection algorithm based on link clustering in complex networks. In: MILCOM (2014)Google Scholar
  15. 15.
    Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition (1995)Google Scholar
  16. 16.
    Hu, R., Aggarwal, C.C., Ma, S., Huai, J.: An embedding approach to anomaly detection. In: 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, 16–20 May 2016, pp. 385–396 (2016)Google Scholar
  17. 17.
    Huang, Z.: Clustering large data sets with mixed numeric and categorical values. In: PAKDD (1997)Google Scholar
  18. 18.
    Jiang, B., et al:. Retweeting behavior prediction based on one-class collaborative filtering in social networks. In: SIGIR (2016)Google Scholar
  19. 19.
    Jiang, B., Liang, J., Sha, Y., Wang, L.: Message clustering based matrix factorization model for retweeting behavior prediction. In: CIKM (2015)Google Scholar
  20. 20.
    Jiang, Z., et al.: Understanding human dynamics in microblog posting activities. J. Stat. Mech: Theory Exp. 2013(02), P02006 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Lim, K.H., Datta, A.: Interest classification of Twitter users using Wikipedia. In: OpenSym (2013)Google Scholar
  22. 22.
    Liu, Z., Chen, X., Sun, M.: Mining the interests of Chinese microbloggers via keyword extraction. Front. Comput. Sci. China 6(1), 76–87 (2012)MathSciNetGoogle Scholar
  23. 23.
    Ma, S., Li, J., Hu, C., Lin, X., Huai, J.: Big graph search: challenges and techniques. Front. Comput. Sci. 10(3), 387–398 (2016)CrossRefGoogle Scholar
  24. 24.
    Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on Twitter: a first look. In: Workshop on Analytics for Noisy Unstructured Text Data, (in conjunction with CIKM) (2010)Google Scholar
  25. 25.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)Google Scholar
  26. 26.
    Park, S., Han, S.P., Huh, S., Lee, H.: Preprocessing uncertain user profile data: inferring user’s actual age from ages of the user’s neighbors. In: ICDE (2009)Google Scholar
  27. 27.
    Pennacchiotti, M., Popescu, A.: A machine learning approach to Twitter user classification. In: ICWSM (2011)Google Scholar
  28. 28.
    Ruan, Y., Fuhry, D., Parthasarathy, S.: Efficient community detection in large networks using content and links. In: WWW (2013)Google Scholar
  29. 29.
    Shiokawa, H., Fujiwara, Y., Onizuka, M.: Fast algorithm for modularity-based graph clustering. In: AAAI (2013)Google Scholar
  30. 30.
    Volkova, S., Coppersmith, G., Durme, B.V.: Inferring user political preferences from streaming communications. In: ACL (2014)Google Scholar
  31. 31.
    Wang, X., et al.: Recommending groups to users using user-group engagement and time-dependent matrix factorization. In: AAAI (2016)Google Scholar
  32. 32.
    Xu, Z., Lu, R., Xiang, L., Yang, Q.: Discovering user interest on Twitter with a modified author-topic model. In: WI (2011)Google Scholar
  33. 33.
    Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: WSDM (2013)Google Scholar
  34. 34.
    Zhang, J., Liu, B., Tang, J., Chen, T., Li, J.: Social influence locality for modeling retweeting behaviors. In: IJCAI (2013)Google Scholar
  35. 35.
    Zhang, Q., Gong, Y., Guo, Y., Huang, X.: Retweet behavior prediction using hierarchical dirichlet process. In: AAAI (2015)Google Scholar
  36. 36.
    Zhang, T., Cui, P., Faloutsos, C., Lu, Y., Ye, H., Zhu, W., Yang, S.: come N go: a dynamic model for social group evolution. TKDD 11(4), 41:1–41:22 (2017)CrossRefGoogle Scholar
  37. 37.
    Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing Twitter and traditional media using topic models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jinhai Zhu
    • 1
    • 2
  • Shuai Ma
    • 1
    • 2
  • Hui Zhang
    • 1
    • 2
  • Chunming Hu
    • 1
    • 2
  • Xiong Li
    • 3
  1. 1.SKLSDE LabBeihang UniversityBeijingChina
  2. 2.Beijing Advanced Innovation Center for Big Data and Brain ComputingBeijingChina
  3. 3.National Computer Network Emergency Response Technical Team/Coordination Center of ChinaBeijingChina

Personalised recommendations