Advertisement

An Analysis of Twitter Messages in the 2011 Tohoku Earthquake

  • Son Doan
  • Bao-Khanh Ho Vo
  • Nigel Collier
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 91)

Abstract

Social media such as Facebook and Twitter have proven to be a useful resource to understand public opinion towards real world events. In this paper, we investigate over 1.5 million Twitter messages (tweets) for the period 9th March 2011 to 31st May 2011 in order to track awareness and anxiety levels in the Tokyo metropolitan district to the 2011 Tohoku Earthquake and subsequent tsunami and nuclear emergencies. These three events were tracked using both English and Japanese tweets. Preliminary results indicated: 1) close correspondence between Twitter data and earthquake events, 2) strong correlation between English and Japanese tweets on the same events, 3) tweets in the native language play an important roles in early warning, 4) tweets showed how quickly Japanese people’s anxiety returned to normal levels after the earthquake event. Several distinctions between English and Japanese tweets on earthquake events are also discussed. The results suggest that Twitter data can be used as a useful resource for tracking the public mood of populations affected by natural disasters as well as an early warning system.

Keywords

Twitter social media earthquake surveillance natural language processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    BBC (2001), http://www.bbc.co.uk/news/business-12889048 (retrieved March 28, 2011)
  2. 2.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. Journal of Computational Science 2(1), 1–8 (2011)CrossRefGoogle Scholar
  3. 3.
    CBS News. New USGS number puts Japan quake at 4th largest, March 14. Associated Press (2011), Archived from the original on (April 5, 2011), http://www.webcitation.org/5xgjFTgf4 (retrieved March 15, 2011)
  4. 4.
    CNN. Japanese PM: ’Toughest’ crisis since World War II (March 13, 2011), Archived from the original on (April 12, 2011), http://edition.cnn.com/2011/WORLD/asiapcf/03/13/japan.quake/index.html?iref=NS1 (retrieved March 13, 2011)
  5. 5.
    Collier, N., Doan, S., Kawazoe, A., Goodwin, R., Conway, M., Tateno, Y., Ngo, Q.-H., Dien, D., Kawtrakul, A., Takeuchi, K., Shigematsu, M., Taniguchi, K.: BioCaster: detecting public health rumors with a Web-based text mining system. Bioinformatics (2008), doi:10.1093/bioinformatics/btn534Google Scholar
  6. 6.
    Collier, N., Doan, S.: Syndromic Classification of Twitter Messages. In: Kostkova, P., Szomszor, M., Fowler, D. (eds.) eHealth 2011. LNICST, vol. 91, pp. 186–195. Springer, Heidelberg (2012)Google Scholar
  7. 7.
    Eysenbach, G.: Infodemiology: Tracking Flu-Related Searches on the Web for Syndromic Surveillance. In: AMIA Annu. Symp. Proc. 2006, pp. 244–248 (2006)Google Scholar
  8. 8.
    Ginsberg, J., Mohebbi, M., Patel, R., Brammer, L., Smolinski, M., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457(7232), 2012–2014 (2009)CrossRefGoogle Scholar
  9. 9.
    Lampos, V., Cristianini, N.: Tracking the flu pandemic by monitoring the social web. In: Proc. of the 2nd International Workshop on Cognitive Information Processing (CIP), pp. 411–416 (2010)Google Scholar
  10. 10.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: Linking text sentiment to public opinion time series. In: Cohen, W.W., Gosling, S. (eds.) Proceedings of the 4th International AAAI Conference on Weblogs and Social Media, pp. 122–129. AAAI Press (2010)Google Scholar
  11. 11.
    Polgreen, P.M., Chen, Y., Pennock, D.M., Nelson, Weinstein, R.: Using internet searches for influenza surveillance. Clinical Infectious Diseases 47(11), 1443–1448 (2008)CrossRefGoogle Scholar
  12. 12.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes twitter users: realtime event detection by social sensors. In: Proc. of the 19th International World Wide Web Conference, Raleigh, NC, USA, pp. 851–860 (2010)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Son Doan
    • 1
  • Bao-Khanh Ho Vo
    • 1
  • Nigel Collier
    • 1
  1. 1.National Institute of InformaticsHitotsubashiJapan

Personalised recommendations