Modeling Flu Trends with Real-Time Geo-tagged Twitter Data Streams

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9204)


The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. To evaluate the accuracy of the influenza estimation based on tweet streams, we correlate the results with official statistics from Center for Disease Control and Prevention (CDC). Our preliminary results have demonstrated that real-time tweet streams capture the dynamics of influenza at national level, and could potentially serve as an early warning system of influenza epidemics or flu trends.


Influenza Mathematical modeling Geo-tagged twitter stream 



This project is supported by NSF grant CNS #1218212.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
  5. 5.
  6. 6.
  7. 7.
    Dredze, M., Paul, M., Bergsma, S., Tran, H.: Carmen: a twitter geolocation system with applications to public health. In: AAAI Workshop on Expanding the Boundaries of Health Informatics Using AI (HIAI) (2012)Google Scholar
  8. 8.
    Lyon, A., Nunn, M., Grossel, G., Burgman, M.: Comparison of web-based biosecurity intelligence systems: biocaster, epispider and healthmap. Transboundary Emerg. Dis. 59(3), 223–232 (2012)CrossRefGoogle Scholar
  9. 9.
    Chew, C., Eysenbach, G.: Pandemics in the age of twitter: content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE 5(11), e14118 (2012)CrossRefGoogle Scholar
  10. 10.
    Signorini, A., Segre, A.M., Polgreen, P.M.: The use of twitter to track levels of disease activity and public concern in the u.s. during the influenza a h1n1 pandemic. PLoS ONE 6(5), e19467 (2011)CrossRefGoogle Scholar
  11. 11.
    Chunara, R., Andrews, J.R., Brownstein, J.S.: Social and news media enable estimation of epidemiological patterns early in the 2010 haitian cholera outbreak. Am. J. Trop. Med. Hyg. 86(1), 39–45 (2012)CrossRefGoogle Scholar
  12. 12.
    Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu: detecting influenza epidemics using Twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing Association for Computational Linguistics, pp. 1568–1576 (2011)Google Scholar
  13. 13.
    Lampos, V., Cristianini, N.: Tracking the flu pandemic by monitoring the social web. In: Proceedings of the 2nd International Workshop on Cognitive Information Processing (CIP), pp. 411–416 (2010)Google Scholar
  14. 14.
    Lee, K., Agrawal, A., Choudhary, A.: Real-time disease surveillance using twitter data: demonstration on flu and cancer. In: Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 11–14 August 2013 Chicago, IL, pp. 1474–1477. ACM (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Mathematical and Natural SciencesArizona State UniversityTempeUSA

Personalised recommendations