Online Event Detection in Social Media with Bursty Event Recognition

  • Wanlun MaEmail author
  • Zhuo LiuEmail author
  • Xiangyu HuEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1095)


The emergence of social media opens tremendous research opportunities. Many individuals, mostly teens and young adults around the world, share their daily lives and opinions about a wide variety of topics (e.g., crime, sports, and politics) on social media sites. Thus, social media becomes a valuable repository for data of different types, which could provide insights of social events happening around the world. However, it is still a challenge to identify the bursty and disruptive events from the massive and noisy user-generated content on social media sites. In this paper, we present a novel event detection framework for identifying surrounding real-world events that can support decision making and emergency management. Our proposed framework consists of four main components, including data pre-processing, event-related tweets classifying, online clustering, and bursty event recognition. We conducted a series of experiments on the real-world social media dataset collected from Twitter. The experimental results demonstrated the effectiveness of our proposed method.


Event detection Social media Text mining Information extraction 


  1. 1.
    Alsaedi, N., Burnap, P.: Arabic event detection in social media. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9041, pp. 384–401. Springer, Cham (2015). Scholar
  2. 2.
    Alsaedi, N., Burnap, P., Rana, O.: Can we predict a riot? Disruptive event detection using twitter. ACM Trans. Internet Technol. (TOIT) 17(2), 18 (2017)CrossRefGoogle Scholar
  3. 3.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on twitter. In: ICWSM, vol. 11, pp. 438–441 (2011)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  6. 6.
    Burnap, P., Williams, M.L., Sloan, L., Rana, O., Housley, W., Edwards, A., Knight, V., Procter, R., Voss, A.: Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack. Soc. Netw. Anal. Min. 4(1), 206 (2014)CrossRefGoogle Scholar
  7. 7.
    Chen, G., Kong, Q., Mao, W.: Online event detection and tracking in social media based on neural similarity metric learning. In: IEEE International Conference on Intelligence and Security Informatics, pp. 182–184 (2017)Google Scholar
  8. 8.
    Chen, Y., Zhang, H., Wu, J., Wang, X., Liu, R., Lin, M.: Modeling emerging, evolving and fading topics using dynamic soft orthogonal NMF with sparse representation. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 61–70. IEEE (2015)Google Scholar
  9. 9.
    Ghenai, A., Mejova, Y.: Catching Zika fever: application of crowdsourcing and machine learning for tracking health misinformation on twitter. arXiv preprint arXiv:1707.03778 (2017)
  10. 10.
    Guille, A., Favre, C.: Mention-anomaly-based event detection and tracking in twitter. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), pp. 375–382. IEEE (2014)Google Scholar
  11. 11.
    Kalyanam, J.: Leveraging social context for modeling topic evolution. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 517–526 (2015)Google Scholar
  12. 12.
    Kleinberg, J.: Bursty and hierarchical structure in streams. Data Min. Knowl. Disc. 7(4), 373–397 (2003)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefGoogle Scholar
  14. 14.
    Liu, J., Chen, Y., Liu, K., Zhao, J.: Event detection via gated multilingual attention mechanism. Statistics 1000, 1250 (2018)Google Scholar
  15. 15.
    Palen, L.: Online social media in crisis events. Educ. Q. 31(3), 76–78 (2008)MathSciNetGoogle Scholar
  16. 16.
    Schinas, M., Papadopoulos, S., Petkos, G., Kompatsiaris, Y., Mitkas, P.A.: Multimodal graph-based event detection and summarization in social media streams. In: ACM International Conference on Multimedia, pp. 189–192 (2015)Google Scholar
  17. 17.
    Shearer, E., Gottfriend, J.: News use across social media platforms 2017. Pew Research Center (2017)Google Scholar
  18. 18.
    Shin, D.S., et al.: STExNMF: spatio-temporally exclusive topic discovery for anomalous event detection. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 435–444. IEEE (2017)Google Scholar
  19. 19.
    Sriram, B., Fuhry, D., Demir, E., Ferhatosmanoglu, H., Demirbas, M.: Short text classification in twitter to improve information filtering. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 841–842. ACM (2010)Google Scholar
  20. 20.
    Xing, C., Wang, Y., Liu, J., Huang, Y., Ma, W.Y.: Hashtag-based sub-event discovery using mutually generative LDA in twitter. In: AAAI, pp. 2666–2672 (2016)Google Scholar
  21. 21.
    Zhao, L., Sun, Q., Ye, J., Chen, F., Lu, C.T., Ramakrishnan, N.: Multi-task learning for spatio-temporal event forecasting. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1503–1512. ACM (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China

Personalised recommendations