Time-Sensitive Topic-Based Communities on Twitter

  • Hossein Fani
  • Fattane Zarrinkalam
  • Ebrahim Bagheri
  • Weichang Du
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9673)

Abstract

This paper tackles the problem of detecting temporal content-based user communities from Twitter. Most existing content-based community detection methods consider the users who share similar topical interests to be like-minded and use this as a basis to group the users. However, such approaches overlook the potential temporality of users’ interests. In this paper, we propose to identify time-sensitive topic-based communities of users who have similar temporal tendency with regards to their topics of interest. The identification of such communities provides the potential for improving the quality of community-level studies, such as personalized recommendations and marketing campaigns that are sensitive to time. To this end, we propose a graph-based framework that utilizes multivariate time series analysis to represent users’ temporal behavior towards their topics of interest in order to identify like-minded users. Further, Topic over Time (TOT) topic model that jointly captures keyword co-occurrences and locality of those patterns over time is utilized to discover users’ topics of interest. Experimental results on our Twitter dataset demonstrates the effectiveness of our proposed temporal approach in the context of personalized news recommendation and timestamp prediction compared to non-temporal community detection methods.

Keywords

Community detection Topic modeling Time series analysis Graph clustering Twitter 

References

  1. 1.
    Abdelbary, H., ElKorany, A., Bahgat, R.: Utilizing deep learning for content-based community detection. In: Science and Information Conference, pp. 777–784 (2014)Google Scholar
  2. 2.
    Abel, F., Gao, Q., Houben, G.-J., Tao, K.: Analyzing user modeling on twitter for personalized news recommendations. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 1–12. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. In: Advances in Neural Information Processing Systems, NIPS 2001, vol. 14, pp. 601–608 (2001)Google Scholar
  4. 4.
    Blondel, V., Guillaume, J., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  5. 5.
    Chen, J.M., Tang, Y., Li, J.G., Mao, C.J., Xiao, J.: Community-based scholar recommendation modeling in academic social network sites. In: Huang, Z., Liu, C., He, J., Huang, G. (eds.) WISE Workshops 2013. LNCS, vol. 8182, pp. 325–334. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  6. 6.
    Deng, Q., Li, Z., Zhang, X., Xia, J.: Interaction-based social relationship type identification in microblog. In: International Workshop on Behavior and Social Informatics and Computing, pp. 151–164 (2013)Google Scholar
  7. 7.
    Ding, Y.: Community detection: topological vs. topical. J. Infometrics 5(4), 498–514 (2011)CrossRefGoogle Scholar
  8. 8.
    Ferragina, P., Scaiella, U.: Fast and accurate annotation of short texts with wikipedia pages. J. IEEE Softw. 29(1), 70–75 (2012)CrossRefGoogle Scholar
  9. 9.
    Hong, L., Davison, B.: Empirical study of topic modeling in twitter. In: 1st ACM Workshop on Social Media Analytics, pp. 80–88 (2010)Google Scholar
  10. 10.
    Hu, Z., Yao, J., Cui, B.: User group oriented temporal dynamics exploration. In: AAAI 2014, pp. 66–72 (2014)Google Scholar
  11. 11.
    Liu, H., Chen, H., Lin, M., Wu, Y.: Community detection based on topic distance in social tagging networks. TELKOMNIKA Indonesian J. Electr. Eng. 12(5), 4038–4049 (2014)Google Scholar
  12. 12.
    Mehrotra, R., Sanner, S., Buntine, W., Xie, L.: Improving lda topic models for microblogs via tweet pooling and automatic labeling. In: SIGIR 2013, pp. 889–892. ACM (2013)Google Scholar
  13. 13.
    Natarajan, N., Sen, P., Chaoji, V.: Community detection in content-sharing social networks. In: ASONAM 2013, pp. 82–89 (2013)Google Scholar
  14. 14.
    Noack, A., Rotta, R.: Multilevel local search algorithms for modularity clustering. In: SEA 2009, pp. 257–268 (2009)Google Scholar
  15. 15.
    Peng, D., Lei, X., Huang, T.: Dich: a framework for discovering implicit communities hidden in tweets. World Wide Web 18(4), 795–818 (2014)CrossRefGoogle Scholar
  16. 16.
    Rosen-Zvi, M., Griffiths, T., Steyvers, M., Smyth, P.: The author-topic model for authors and documents. In: UAI 2004, pp. 487–494 (2004)Google Scholar
  17. 17.
    Sachan, M., Contractor, D., Faruquie, T., Subramaniam, L.: Using content and interactions for discovering communities in social networks. In: WWW 2012, pp. 331–340 (2012)Google Scholar
  18. 18.
    Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: SIGIR 10, pp. 841–842 (2010)Google Scholar
  19. 19.
    Wang, X., McCallum, A.: Topics over time: a non-markov continuous-time model of topical trends. In: KDD 2006, pp. 424–433 (2006)Google Scholar
  20. 20.
    Yin, Z., Cao, L., Gu, Q., Han, J.: Latent community topic analysis: integration of community discovery with topic modeling. ACM Trans. Intell. Syst. Technol. 3(4), 63:1–63:21 (2012)CrossRefGoogle Scholar
  21. 21.
    Zarrinkalam, F., Fani, H., Bagheri, E., Kahani, M., Du, W.: Semantics-enabled user interest detection from twitter. In: WI-IAT 2015, pp. 469–476 (2015)Google Scholar
  22. 22.
    Zhao, G., Lee, M., Hsu, W., Chen, W., Hu, H.: Community-based user recommendation in uni-directional social networks. In: CIKM 2015, pp. 189–198 (2013)Google Scholar
  23. 23.
    Zhou, D., Manavoglu, E., Li, J., Giles, C., Zha, H.: Probabilistic models for discovering e-communities. In: WWW 2006, pp. 173–182 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hossein Fani
    • 1
    • 3
  • Fattane Zarrinkalam
    • 1
    • 2
  • Ebrahim Bagheri
    • 1
  • Weichang Du
    • 3
  1. 1.Laboratory for Systems, Software and Semantics (LS3)Ryerson UniversityTorontoCanada
  2. 2.Department of Computer EngineeringFerdowsi University of MashhadMashhadIran
  3. 3.Faculty of Computer ScienceUniversity of New BrunswickFrederictonCanada

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