Integration and Dissemination of Citizen Reported and Seismically Derived Earthquake Information via Social Network Technologies

  • Michelle Guy
  • Paul Earle
  • Chris Ostrum
  • Kenny Gruchalla
  • Scott Horvath
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6065)

Abstract

People in the locality of earthquakes are publishing anecdotal information about the shaking within seconds of their occurrences via social network technologies, such as Twitter. In contrast, depending on the size and location of the earthquake, scientific alerts can take between two to twenty minutes to publish. We describe TED (Twitter Earthquake Detector) a system that adopts social network technologies to augment earthquake response products and the delivery of hazard information. The TED system analyzes data from these social networks for multiple purposes: 1) to integrate citizen reports of earthquakes with corresponding scientific reports 2) to infer the public level of interest in an earthquake for tailoring outputs disseminated via social network technologies and 3) to explore the possibility of rapid detection of a probable earthquake, within seconds of its occurrence, helping to fill the gap between the earthquake origin time and the presence of quantitative scientific data.

Keywords

Twitter micro-blogging social network citizen reporting earthquake hazard geospatial-temporal data time series 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Earle, P., Guy, M., Buckmaster, R., Ostrum, C., Horvath, S., Vaughan, A.: OMG Earthquake! Can Twitter Improve earthquake response? Seismological Research Letters (to appear 2010)Google Scholar
  2. 2.
    O’Reilly, T., Milstein, S.: The Twitter Book. O’Reilly Media, Inc., Sebastopol (2009)Google Scholar
  3. 3.
    Cheong, M., Lee, V.: Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base. In: SWSM 2009: Proceeding of the 2nd ACM workshop on Social web search and mining, pp. 1–8 (2009)Google Scholar
  4. 4.
    Zhao, D., Rosson, M.B.: How and why people Twitter: the role that micro-blogging plays in informal communication at work. In: Proceedings of the ACM 2009 international conference on Supporting group work, pp. 243–252 (2009)Google Scholar
  5. 5.
    Java, A., Song, X., Finin, T., Tseng, B.: Why We Twitter: An Analysis of a Microblogging Community. In: Advances in Web Mining and Web Usage Analysis, pp. 118–138 (2009)Google Scholar
  6. 6.
    Honeycutt, C., Herring, S.: Beyond Microblogging: Conversation and Collaboration via Twitter. In: HICSS 2009: Proceedings of the 42nd Hawaii International Conference on System Sciences, pp. 1–10 (2009)Google Scholar
  7. 7.
    Dixon, J., Tucker, C.R.: We use technology, but do we use technology? using existing technologies to communicate, collaborate, and provide support. In: SIGUCCS 2009: Proceedings of the ACM SIGUCCS fall conference on User services conference, pp. 309–312 (2009)Google Scholar
  8. 8.
    McNely, B.: Backchannel persistence and collaborative meaning-making. In: SIGDOC 2009: Proceedings of the 27th ACM international conference on Design of communication, pp. 297–304 (2009)Google Scholar
  9. 9.
    Starbird, K., Palen, L., Hughes, A., Vieweg, S.: Chatter on The Red: What Hazards Threat Reveals about the Social Life of Microblogged Information. In: CSCW 2010: Proceedings of the ACM 2010 Conference on Computer Supported Cooperative Work (2010)Google Scholar
  10. 10.
    Hughes, A., Palen, L.: Twitter Adoption and Use in Mass Convergence and Emergency Events. In: ISCRAM 2009: Proceedings of the 2009 Information Systems for Crisis Response and Management Conference (2009)Google Scholar
  11. 11.
    Vieweg, S., Palen, L., Sophia, L., Hughes, A.: Collective Intelligence in Disaster: An Examination of the Phenomenon in the Aftermath of the 2007 Virginia Tech Shootings. In: ISCRAM 2008: Proceedings of the Information Systems for Crisis Response and Management Conference (2009)Google Scholar
  12. 12.
    De Longueville, B., Smith, R.S., Luraschi, G.: OMG, from here, I can see the flames!: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: LBSN 2009: Proceedings of the 2009 International Workshop on Location Based Social Networks, pp. 73–80 (2009)Google Scholar
  13. 13.
    Gruhl, D., Guha, R., Kumar, R., Novak, J., Tomkins, A.: The predictive power of online chatter. In: KDD 2005: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 78–87 (2005)Google Scholar
  14. 14.
    Phelan, O., McCarthy, K., Smyth, B.: Using twitter to recommend real-time topical news. In: RecSys 2009: Proceedings of the third ACM conference on Recommender systems, pp. 385–388 (2009)Google Scholar
  15. 15.
    Wald, D.J., Quitoriano, V., Dengler, L., Dewey, J.W.: Utilization of the Internet for Rapid Community Intensity Maps. Seismological Research Letters 70, 680–697 (1999)Google Scholar
  16. 16.
    Wood, H.O., Neumann, F.: Modified Mercalli Intensity Scale of 1931. Bulletin of the Seismological Society of America 21, 227–283 (1931)Google Scholar
  17. 17.
  18. 18.
  19. 19.
    The Great California Shake Out, http://www.shakeout.org

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Michelle Guy
    • 1
  • Paul Earle
    • 1
  • Chris Ostrum
    • 1
  • Kenny Gruchalla
    • 2
  • Scott Horvath
    • 3
  1. 1.U.S. Geological Survey National Earthquake Information CenterGoldenUSA
  2. 2.National Renewable Energy LaboratoryGoldenUSA
  3. 3.U.S. Geological Survey National Earthquake Information CenterRestonUSA

Personalised recommendations