Detecting Bursty Topics of Correlated News and Twitter for Government Services

  • Takehito Utsuro
  • Yusuke Inoue
  • Takakazu Imada
  • Masaharu Yoshioka
  • Noriko Kando
Chapter

Abstract

This chapter presents a framework of detecting bursty topics of correlated news and twitter, and discusses how to integrate the framework into government services. Especially, as a specific application of the proposed framework of detecting bursty topics of correlated news and twitter, this chapter gives an example of collecting news and twitter that are related to “the 2012 London Olympic game” and applying the proposed framework.

Keywords

Time series news and twitter Topic model Kleinberg’s burst model 

References

  1. 1.
    Kleinberg, J. (2002). Bursty and hierarchical structure in streams. In Proceedings of 8th SIGKDD (pp. 91–101).Google Scholar
  2. 2.
    Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.MATHGoogle Scholar
  3. 3.
    Blei, D. M., & Lafferty, J. D. (2006). Dynamic topic models. In Proceedings of 23rd ICML (pp. 113–120).Google Scholar
  4. 4.
    Takahashi, Y., Utsuro, T., Yoshioka, M., Kando, N., Fukuhara, T., Nakagawa, H., & Kiyota, Y. (2012). Applying a burst model to detect bursty topics in a topic model. In JapTAL 2012 (Vol. 7614 of LNCS, pp. 239–249) Berlin: Springer.Google Scholar
  5. 5.
    Mane, K., & Borner, K. (2004). Mapping topics and topic bursts in PNAS. In: Proceedings of PNAS (Vol. 101, Suppl 1, pp. 5287–5290).Google Scholar
  6. 6.
    AlSumait, L., Bardara, D., Gentle, J., & Domeniconi, C. (2009). Topic significance ranking of LDA generative models. In Proceedings of ECML/PKDD (pp. 67–82).Google Scholar
  7. 7.
    Wang, X., Zhai, C. X., & Hu, R. S. (2007). Mining correlated bursty topic patterns from coordinated text streams. In Proceedings of 13th SIGKDD (pp. 784–793).Google Scholar
  8. 8.
    Zhang, J., Song, Y., Zhang, C., & Liu, S. (2010). Evolutionary hierarchical Dirichlet processes for multiple correlated time-varying corpora. In Proceedings of 16th SIGKDD (pp. 1079–10881).Google Scholar
  9. 9.
    Petrović, S., Osborne, M., & Lavrenko, V. (2010). Streaming first story detection with application to twitter. In HLT-NAACL (pp. 181–189).Google Scholar
  10. 10.
    Weng, J., & Lee, B. S. (2011). LDA-Based document models for ad-hoc retrieval. In Proceedings of Fifth ICWSM (pp. 401–408).Google Scholar
  11. 11.
    Li, C., Sun, A., & Datta, A. (2012). Twevent: Segment-based event detection from tweets. In Proceedings of 21st CIKM (pp. 155–164).Google Scholar
  12. 12.
    Diao, Q., Jiang, J., Zhu, F., & Lim, E. P. (2012). Finding bursty topics from microblogs. In Proceedings of 50th ACL (pp. 536–544).Google Scholar
  13. 13.
    AlSumait, L., Bardara, D., & Domeniconi, C. (2008). On-Line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking. In Proceedings of 8th ICDM (pp. 3–12).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Takehito Utsuro
    • 1
  • Yusuke Inoue
    • 1
  • Takakazu Imada
    • 1
  • Masaharu Yoshioka
    • 2
  • Noriko Kando
    • 3
  1. 1.University of TsukubaTsukubaJapan
  2. 2.Hokkaido UniversitySapporoJapan
  3. 3.National Institute of InformaticsTokyoJapan

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