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Real-Time Entity-Based Event Detection for Twitter

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9283)

Abstract

In recent years there has been a surge of interest in using Twitter to detect real-world events. However, many state-of-the-art event detection approaches are either too slow for real-time application, or can detect only specific types of events effectively. We examine the role of named entities and use them to enhance event detection. Specifically, we use a clustering technique which partitions documents based upon the entities they contain, and burst detection and cluster selection techniques to extract clusters related to on-going real-world events. We evaluate our approach on a large-scale corpus of 120 million tweets covering more than 500 events, and show that it is able to detect significantly more events than current state-of-the-art approaches whilst also improving precision and retaining low computational complexity. We find that nouns and verbs play different roles in event detection and that the use of hashtags and retweets lead to a decreases in effectiveness when using our entity-base approach.

Keywords

Event detection Social media Reproducibility Twitter 

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References

  1. 1.
    Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: Proc. of SDM Conference (2012)Google Scholar
  2. 2.
    Allan, J., Harding, S., Fisher, D., Bolivar, A., Guzman-Lara, S., Amstutz, P.: Taking topic detection from evaluation to practice. In: HICSS 2005. IEEECS, Washington, D.C. (2005)Google Scholar
  3. 3.
    Allan, J., Lavrenko, V., Jin, H.: First story detection in TDT is hard. In: CIKM 2000, pp. 374–381. ACM, New York (2000)Google Scholar
  4. 4.
    Allan, J., Lavrenko, V., Malin, D., Swan, R.: Detections, bounds, and timelines: UMass and TDT-3. In: TDT-3 Workshop (2000)Google Scholar
  5. 5.
    Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Computational Intelligence (2013)Google Scholar
  6. 6.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on twitter. In: ICWSM 2011 (2011)Google Scholar
  7. 7.
    Choudhury, S., Breslin, J.G.: Extracting semantic entities and events from sports tweets. In: Proceedings of #MSM2011 at ESWC (2011)Google Scholar
  8. 8.
    Derczynski, L., Ritter, A., Clark, S., Bontcheva, K.: Twitter part-of-speech tagging for all: overcoming sparse and noisy data. In: ICRA-NLP (2013)Google Scholar
  9. 9.
    Hu, M., Liu, S., Wei, F., Wu, Y., Stasko, J., Ma, K.-L.: Breaking news on twitter. In: CHI 2012. ACM, New York (2012)Google Scholar
  10. 10.
    Kumaran, G., Allan, J.: Text classification and named entities for new event detection. In: SIGIR 2004, pp. 297–304. ACM, New York (2004)Google Scholar
  11. 11.
    Kumaran, G., Allan, J.: Using names and topics for new event detection. In: HLT 2005, pp. 121–128. ACL, Stroudsburg (2005)Google Scholar
  12. 12.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: WWW 2010. ACM, New York (2010)Google Scholar
  13. 13.
    Li, C., Weng, J., He, Q., Yao, Y., Datta, A., Sun, A., Lee, B.-S.: Twiner: named entity recognition in targeted twitter stream. In: SIGIR (2012)Google Scholar
  14. 14.
    Liu, X., Zhang, S., Wei, F., Zhou, M.: Recognizing named entities in tweets. In: HLT 2011. ACL, Stroudsburg (2011)Google Scholar
  15. 15.
    McMinn, A.J., Moshfeghi, Y., Jose, J.M.: Building a large-scale corpus for evaluating event detection on twitter. In: CIKM 2013. ACM (2013)Google Scholar
  16. 16.
    Osborne, M., Petrovic, S., McCreadie, R., Macdonald, C., Ounis, I.: Bieber no more: first story detection using twitter and wikipedia. In: SIGIR 2012 Workshop TAIA (2012)Google Scholar
  17. 17.
    Ozdikis, O., Senkul, P., Oguztzn, H.: Semantic expansion of tweet contents for enhanced event detection in twitter. In: ASONAM. IEEE CS (2012)Google Scholar
  18. 18.
    Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In HLT 2010. ACL (2010)Google Scholar
  19. 19.
    Pukelsheim, F.: The three sigma rule. The American Statistician 48(2), 88–91 (1994)MathSciNetGoogle Scholar
  20. 20.
    Ritter, A., Mausam, Etzioni, O., Clark, S.: Open domain event extraction from twitter. In: Proceedings of ACM SIGKDD 2012. ACM (2012)Google Scholar
  21. 21.
    Sankaranarayanan, J., Samet, H., Teitler, B., Lieberman, M., Sperling, J.: Twitterstand: news in tweets. In: ACM SIGSPATIAL 2009 (2009)Google Scholar
  22. 22.
    Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: SIGIR 1998, pp. 28–36. ACM, New York (1998)Google Scholar
  23. 23.
    Yang, Y., Zhang, J., Carbonell, J., Jin, C.: Topic-conditioned novelty detection. In: ACM CIKM 2002, pp. 688–693 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computing ScienceUniversity of GlasgowGlasgowScotland, UK

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