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Life Activity Modeling of News Event on Twitter Using Energy Function

  • Rong Lu
  • Zhiheng Xu
  • Yang Zhang
  • Qing Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7302)

Abstract

This research is the first exploration on modeling life activity of news event on Twitter. We consider a news event as a natural life form, and use an energy function to evaluate its activity. A news event on Twitter becomes more active with a burst of tweets discussing it, and it fades away with time. These changes of the activity are well captured by the energy function. Then, we incorporate this energy function into the traditional single-pass clustering algorithm, and propose a more adaptive on-line news event detection method. A corpus of tweets which discuss news events was analyzed using our method. Experimental results show that our method not only compares favorably to those of other methods in official TDT measures like precision, recall etc., but also has better time and memory performance, which makes it more suitable for a real system.

Keywords

life activity modeling energy function Twitter news event detection single-pass clustering 

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References

  1. 1.
    Milstein, S., Chowdhury, A., Hochmuth, G., Lorica, B., Magoulas, R.: Twitter and the micro-messaging revolution: Communication, connections, and immediacy–140 characters at a time. O’Reilly Radar Report (2008)Google Scholar
  2. 2.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW 2010, pp. 591–600 (2010)Google Scholar
  3. 3.
    Huberman, B.A., Romero, D.M., Wu, F.: Social networks that matter: Twitter under the microscope. Social Computing Laboratory, HP Labs (2008)Google Scholar
  4. 4.
    Weng, J., Lim, E.P., Jiang, J., He, Q.: TwitterRank: finding topic-sensitive influential twitterers. In: WSDM 2010, pp. 261–270 (2010)Google Scholar
  5. 5.
    Allan, J., Carbonell, J., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study: Final report. In: Proc. of the DARPA Broadcast News Transcription and Understanding Workshop (1998)Google Scholar
  6. 6.
    Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: SIGIR 1998, pp. 28–36 (1998)Google Scholar
  7. 7.
    Chen, C.C., Chen, Y.-T., Sun, Y., Chen, M.C.: Life Cycle Modeling of News Events Using Aging Theory. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 47–59. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Lu, R., Yang, Q.: Extracting News Topics from Microblogs based on Hidden topics discovering and Text Clustering. In: CCIR 2010, pp. 291–298 (2010)Google Scholar
  9. 9.
    Salton, G.: Automatic text processing: the transformation, analysis, and retrieval of information by computer. Addison-Wesley Longman Publishing Co., Inc. (1989)Google Scholar
  10. 10.
    Newman, M.E.J., Park, J.: Why social networks are different from other types of networks. Phys. Rev. E 68(3), 36–122 (2003)CrossRefGoogle Scholar
  11. 11.
    Java, A., Song, X., Finin, T., Tseng, B.: Why we twitter: understanding microblogging usage and communities. In: Proc. of 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65 (2007)Google Scholar
  12. 12.
    Honey, C., Herring, S.C.: Beyond Microblogging: Conversation and Collaboration via Twitter. In: Proc. of the 42nd Hawaii International Conference on System Sciences, pp. 1–10 (2009)Google Scholar
  13. 13.
    Zhao, D., Rosson, M.B.: How and why people Twitter: the role that micro-blogging plays in informal communication at work. In: Proc. of the ACM 2009 International Conference on Supporting Group Work, pp. 243–252 (2009)Google Scholar
  14. 14.
    Coon, M., Reeves, B.: Social Media Marketing: Successful Case Studies of Businesses Using Facebook and YouTube With An In-Depth Look into the Business Use of Twitter (2010)Google Scholar
  15. 15.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: WWW 2010, pp. 851–860 (2010)Google Scholar
  16. 16.
    Menczer, F., Belew, R.K., Willuhn, W.: Artificial Life Applied to Adaptive Information Agents. In: AAAI 1995 (1995)Google Scholar
  17. 17.
    Kempe, D., Kleinberg, J., Tardos, É.: Influential Nodes in a Diffusion Model for Social Networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Leavitt, A., Burchard, E., Fisher, D., Gillbert, S.: New approaches for analyzing influence on twitter. A publication of the Web Ecology project (2009)Google Scholar
  19. 19.
    Brin, S., Page, L.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Network and ISDN Systems, 107–117 (1998)Google Scholar
  20. 20.
    Papka, R., Allan, J.: On-Line New Event Detection using Single Pass Clustering. University of Massachusetts, Amherst (1998)Google Scholar
  21. 21.
    Van Rijsbergen, C.J.: Information Retereval, 2nd edn. Butterworths, Massachusetts (1979)Google Scholar
  22. 22.
    Cataldi, M., Di Caro, L., Schifanella, C.: Emerging topic detection on Twitter based on temporal and social terms evaluation. In: Proc. of the Tenth International Workshop on Multimedia Data Mining, pp. 1–10 (2010)Google Scholar
  23. 23.
    Phuvipadawat, S., Murata, T.: Breaking News Detection and Tracking in Twitter. In: International Conference on Web Intelligence and Intelligent Agent Technology, pp. 120–123 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Rong Lu
    • 1
  • Zhiheng Xu
    • 1
  • Yang Zhang
    • 1
  • Qing Yang
    • 1
  1. 1.Institute of AutomationChinese Academy of ScienceBeijingChina

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