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SensorTree: Bursty Propagation Trees as Sensors for Protest Event Detection

  • Jeffery Ansah
  • Wei Kang
  • Lin Liu
  • Jixue Liu
  • Jiuyong Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11233)

Abstract

Protest event detection is an important task with numerous benefits to many organisations, emergency services, and other stakeholders. Existing research has presented myriad approaches relying on tweet corpus to solve the event detection problem, with notable improvements over time. Despite the plethora of research on event detection, the use of the implicit social links among users in online communities for event detection is rarely observed. In this work, we propose SensorTree, a novel event detection framework that utilizes the network structural connections among users in a community for protest event detection. SensorTree tracks information propagating among communities of Twitter users as propagation trees to detect bursts based on the sudden changes in size of these communities. Once a burst is identified, SensorTree uses a latent event topic model to extract topics from the corpus over the burst period to describe the event that triggered the burst. Extensive experiments performed on real-world Twitter datasets using qualitative and quantitative evaluations show the superiority of SensorTree over existing state-of-the-art methods. We present case studies to further show that SensorTree detects events with fine granularity descriptions.

Keywords

Burst Event detection Propagation trees Social media Twitter 

Notes

Acknowledgements

We acknowledge Data to Decisions CRC (D2DCRC), Cooperative Research Centres Programme, and the University of South Australia for funding this research. The work has also been partially supported by ARC Discovery project DP170101306.

References

  1. 1.
    Abdelhaq, H., Sengstock, C., Gertz, M.: EvenTweet: online localized event detection from twitter. PVLDB 6(12), 1326–1329 (2013)Google Scholar
  2. 2.
    Aggarwal, C.C., Subbian, K.: Event detection in social streams. In: Proceedings of the 2012 SDM, pp. 624–635. SIAM (2012)Google Scholar
  3. 3.
    Anandkumar, A., Ge, R., Hsu, D., Kakade, S.M., Telgarsky, M.: Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15(1), 2773–2832 (2014)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Ansah, J., Kang, W., Liu, L., Liu, J., Li, J.: Information propagation trees for protest event prediction. In: Phung, D., Tseng, V.S., Webb, G.I., Ho, B., Ganji, M., Rashidi, L. (eds.) PAKDD 2018. LNCS (LNAI), vol. 10939, pp. 777–789. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-93040-4_61CrossRefGoogle Scholar
  5. 5.
    Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: 3rd ACM WSDM, pp. 291–300. ACM (2010)Google Scholar
  6. 6.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on twitter. ICWSM 11(2011), 438–441 (2011)Google Scholar
  7. 7.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)Google Scholar
  8. 8.
    Cadena, J., Korkmaz, G., Kuhlman, C.J., Marathe, A., Ramakrishnan, N., Vullikanti, A.: Forecasting social unrest using activity cascades. PloS one 10(6), e0128879 (2015)CrossRefGoogle Scholar
  9. 9.
    Chen, F., Neill, D.B.: Non-parametric scan statistics for event detection and forecasting in heterogeneous social media graphs. In: Proceedings of the 20th ACM SIGKDD, pp. 1166–1175. ACM (2014)Google Scholar
  10. 10.
    He, D., Parker, D.S.: Topic dynamics: an alternative model of bursts in streams of topics. In: Proceedings of 16th ACM SIGKDD, pp. 443–452. ACM (2010)Google Scholar
  11. 11.
    Huang, F., Niranjan, U., Hakeem, M.U., Anandkumar, A.: Online tensor methods for learning latent variable models. J. Mach. Learn. Res. 16(1), 2797–2835 (2015)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Ihler, A., Hutchins, J., Smyth, P.: Adaptive event detection with time-varying poisson processes. In: Proceedings of 12th ACM SIGKDD, pp. 207–216 (2006)Google Scholar
  13. 13.
    Kleinberg, J.: Bursty and hierarchical structure in streams. Data Min. Knowl. Discov. 7(4), 373–397 (2003)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kontostathis, A., Galitsky, L.M., Pottenger, W.M., Roy, S., Phelps, D.J.: A survey of emerging trend detection in textual data mining. In: Berry, M.W. (ed.) Survey of Text Mining, pp. 185–224. Springer, New York (2004).  https://doi.org/10.1007/978-1-4757-4305-0_9CrossRefGoogle Scholar
  15. 15.
    Li, J., Wen, J., Tai, Z., Zhang, R., Yu, W.: Bursty event detection from microblog: a distributed and incremental approach. Concurr. Comput. Pract. Exp. 28(11), 3115–3130 (2016)CrossRefGoogle Scholar
  16. 16.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: 19th WWW, pp. 851–860. ACM (2010)Google Scholar
  17. 17.
    Weng, J., Lee, B.S.: Event detection in twitter. ICWSM 11, 401–408 (2011)Google Scholar
  18. 18.
    Xie, W., Zhu, F., Jiang, J., Lim, E.P., Wang, K.: TopicSketch: real-time bursty topic detection from twitter. IEEE TKDE 28(8), 2216–2229 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Information Technology and Mathematical SciencesUniversity of South AustraliaAdelaideAustralia

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