Twitter Event Detection in a City

  • Martín SteglichEmail author
  • Raúl Speroni
  • Juan José Prada
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)


Large cities and metropolitan areas are complex systems with connections between their environments and individuals. Citizens express themselves daily about events related to the city on the Internet. This information has great value due to its freshness, diversity of points of view and impact on public opinion.

Information technologies allow us to imagine other types of interfaces for communication between people and institutions. Interfaces capable of extracting useful information even if it is not directed to the corresponding institutions.

In this work a framework that combines different techniques for the events extraction in a city from social networks is built. Using the city of Montevideo as a case study and its waste management as a domain, it was possible to correctly identify 94% of the events reported with only 4% false positives.


Smart city Event extraction Event detection Social networks Twitter Natural language processing Machine learning 


  1. 1.
    Alqhtani, S.M., Luo, S., Regan, B.: Fusing text and image for event detection in twitter. arXiv preprint arXiv:1503.03920 (2015)
  2. 2.
    Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. Comput. Intell. 31(1), 132–164 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bouskela, M.: La ruta hacia las smart cities, January 2017. Accessed 03 June 2018
  4. 4.
    ICT Facts: Figures-the world in 2015. The International Telecommunication Union (ITU), Geneva (2015)Google Scholar
  5. 5.
    Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fung, G.P.C., Yu, J.X., Yu, P.S., Lu, H.: Parameter free bursty events detection in text streams. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 181–192. VLDB Endowment (2005)Google Scholar
  7. 7.
    García Cumbreras, M.Á., Martínez Cámara, E., Villena Román, J., García Morera, J.: Tass 2015-the evolution of the spanish opinion mining systems (2016)Google Scholar
  8. 8.
    Goorha, S., Ungar, L.: Discovery of significant emerging trends. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 57–64. ACM (2010)Google Scholar
  9. 9.
    He, Q., Chang, K., Lim, E.P.: Analyzing feature trajectories for event detection. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 207–214. ACM (2007)Google Scholar
  10. 10.
    He, Q., Chang, K., Lim, E.P., Zhang, J.: Bursty feature representation for clustering text streams. In: SDM, pp. 491–496. SIAM (2007)Google Scholar
  11. 11.
    Ho, T.K.: Random decision forests. In: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)Google Scholar
  12. 12.
    Kleinberg, J.: Bursty and hierarchical structure in streams. Data Min. Knowl. Discov. 7(4), 373–397 (2003)MathSciNetCrossRefGoogle Scholar
  13. 13.
    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). Scholar
  14. 14.
    Popescu, A.M., Pennacchiotti, M.: Detecting controversial events from twitter. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1873–1876. ACM (2010)Google Scholar
  15. 15.
    Radar, G.: Perfil internauta uruguayo 2016 - resumen ejecutivo, January 2017. Accessed 03 June 2018
  16. 16.
    Raúl Speroni, M.S.: Extracción de eventos en una ciudad a partir de redes sociales. Accessed 19 July 2018
  17. 17.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (2010)Google Scholar
  18. 18.
    Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 42–51. ACM (2009)Google Scholar
  19. 19.
    Snowsill, T., Nicart, F., Stefani, M., De Bie, T., Cristianini, N.: Finding surprising patterns in textual data streams. In: 2010 2nd International Workshop on Cognitive Information Processing, pp. 405–410. IEEE (2010)Google Scholar
  20. 20.
    Wang, X., Zhai, C., Hu, X., Sproat, R.: Mining correlated bursty topic patterns from coordinated text streams. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 784–793. ACM (2007)Google Scholar
  21. 21. Twitter usage statistics. Accessed 1 June 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Martín Steglich
    • 1
    Email author
  • Raúl Speroni
    • 1
  • Juan José Prada
    • 1
  1. 1.Facultad de IngenieríaUniversidad de la RepúblicaMontevideoUruguay

Personalised recommendations