Event Detection and Summarization Using Phrase Network

  • Sara Melvin
  • Wenchao YuEmail author
  • Peng Ju
  • Sean Young
  • Wei WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10536)


Identifying events in real-time data streams such as Twitter is crucial for many occupations to make timely, actionable decisions. It is however extremely challenging because of the subtle difference between “events” and trending topics, the definitive rarity of these events, and the complexity of modern Internet’s text data. Existing approaches often utilize topic modeling technique and keywords frequency to detect events on Twitter, which have three main limitations: (1) supervised and semi-supervised methods run the risk of missing important, breaking news events; (2) existing topic/event detection models are base on words, while the correlations among phrases are ignored; (3) many previous methods identify trending topics as events. To address these limitations, we propose the model, PhraseNet, an algorithm to detect and summarize events from tweets. To begin, all topics are defined as a clustering of high-frequency phrases extracted from text. All trending topics are then identified based on temporal spikes of the phrase cluster frequencies. PhraseNet thus filters out high-confidence events from other trending topics using number of peaks and variance of peak intensity. We evaluate PhraseNet on a three month duration of Twitter data and show the both the efficiency and the effectiveness of our approach.


Event detection Phrase network Event summarization 



The work is partially supported by NIH U01HG008488, NIH R01GM115833, NIH U54GM114833, and NSF IIS-1313606. We thank the anonymous reviewers for their careful reading and insightful comments on our manuscript.


  1. 1.
    Agarwal, M.K., Ramamritham, K., Bhide, M.: Real time discovery of dense clusters in highly dynamic graphs: identifying real world events in highly dynamic environments. VLDB 5(10), 980–991 (2012)Google Scholar
  2. 2.
    Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 2008(10), P10008 (2008)CrossRefGoogle Scholar
  3. 3.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRefGoogle Scholar
  4. 4.
    Chua, F.C.T., Asur, S.: Automatic summarization of events from social media. In: ICWSM (2013)Google Scholar
  5. 5.
    Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M., Song, L.: Recurrent marked temporal point processes: embedding event history to vector. In: KDD, pp. 1555–1564. ACM (2016)Google Scholar
  6. 6.
    El-Kishky, A., Song, Y., Wang, C., Voss, C.R., Han, J.: Scalable topical phrase mining from text corpora. VLDB 8(3), 305–316 (2014)Google Scholar
  7. 7.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: SIGMOD, vol. 29, pp. 1–12. ACM (2000)Google Scholar
  8. 8.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW, pp. 591–600. ACM (2010)Google Scholar
  9. 9.
    Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: CIKM, pp. 155–164. ACM (2012)Google Scholar
  10. 10.
    Lin, C.X., Zhao, B., Mei, Q., Han, J.: PET: a statistical model for popular events tracking in social communities. In: KDD, pp. 929–938. ACM (2010)Google Scholar
  11. 11.
    Mathioudakis, M., Koudas, N.: TwitterMonitor: trend detection over the Twitter stream. In: SIGMOD, pp. 1155–1158. ACM (2010)Google Scholar
  12. 12.
    Popescu, A.-M., Pennacchiotti, M.: Detecting controversial events from Twitter. In: CIKM, pp. 1873–1876. ACM (2010)Google Scholar
  13. 13.
    Popescu, A.-M., Pennacchiotti, M., Paranjpe, D.: Extracting events and event descriptions from Twitter. In: WWW, pp. 105–106. ACM (2011)Google Scholar
  14. 14.
    Qin, Y., Zhang, Y., Zhang, M., Zheng, D.: Feature-rich segment-based news event detection on Twitter. In: IJCNLP, pp. 302–310 (2013)Google Scholar
  15. 15.
    Ritter, A., Etzioni, O., Clark, S., et al.: Open domain event extraction from Twitter. In: KDD, pp. 1104–1112. ACM (2012)Google Scholar
  16. 16.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: WWW, pp. 851–860. ACM (2010)Google Scholar
  17. 17.
    Saraf, P., Ramakrishnan, N.: EMBERS AutoGSR: automated coding of civil unrest events. In: KDD, pp. 599–608. ACM (2016)Google Scholar
  18. 18.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment in Twitter events. J. Assoc. Inf. Sci. Technol. 62(2), 406–418 (2011)CrossRefGoogle Scholar
  19. 19.
    Xie, W., Zhu, F., Jiang, J., Lim, E.-P., Wang, K.: TopicSketch: real-time bursty topic detection from Twitter. TKDE 28(8), 2216–2229 (2016)Google Scholar
  20. 20.
    Yu, W., Aggarwal, C.C., Wang, W.: Temporally factorized network modeling for evolutionary network analysis. In: WSDM, pp. 455–464. ACM (2017)Google Scholar
  21. 21.
    Zhao, L., Ye, J., Chen, F., Lu, C.-T., Ramakrishnan, N.: Hierarchical incomplete multi-source feature learning for spatiotemporal event forecasting. In: KDD, pp. 2085–2094. ACM (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUCLALos AngelesUSA
  2. 2.University of California Institute for Prediction Technology, UCLALos AngelesUSA

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