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Propagating Disaster Warnings on Social and Digital Media

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)

Abstract

A nexus of techniques including information extraction techniques, including a bag of words model, web and social media search and time series analysis, are discussed that may reveal the potential of social media and social networks. Social aspects of data privacy are discussed to ensuring that the data collected, filtered, and then used. This work is the effort of Trinity College Dublin and other universities.

Keywords

Information extraction Sentiment analysis Web and social media search Security and privacy Time series analysis 

Notes

Acknowledgments

The authors would like to thank the EU sponsored Slaindail Project (FP7 Security sponsored project #6076921) and Xiubo Zhang for use of the CiCui system.

References

  1. 1.
    Bird, S.: Nltk: the natural language toolkit. In: Proceedings of the COLING/ACL on Interactive presentation sessions, pp. 69–72. Association for Computational Linguistics (2006)Google Scholar
  2. 2.
    Huber, P.J.: Robust Statistics. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  3. 3.
    Hughes, A.L., Palen, L.: Twitter adoption and use in mass convergence and emergency events. Int. J. Emerg. Manage. 6(3), 248–260 (2009)CrossRefGoogle Scholar
  4. 4.
    Hui, C., Tyshchuk, Y., Wallace, W.A., Magdon-Ismail, M., Goldberg, M.: Information cascades in social media in response to a crisis: a preliminary model and a case study. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 653–656. ACM (2012)Google Scholar
  5. 5.
    Kelly, S., Ahmad, K.: Determining levels of urgency and anxiety during a natural disaster: noise, affect, and news in social media. In: DIMPLE: DIsaster Management and Principled Large-Scale information Extraction Workshop Programme, p. 70Google Scholar
  6. 6.
    Rosner, B.: Percentage points for a generalized esd many-outlier procedure. Technometrics 25(2), 165–172 (1983)CrossRefMATHGoogle Scholar
  7. 7.
    Stone, P.J., Dunphy, D.C., Smith, M.S.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press, Cambridge (1966)Google Scholar
  8. 8.
    Székely, G.J., Rizzo, M.L.: Energy statistics: a class of statistics based on distances. J. Stat. Plann. Infer. 143(8), 1249–1272 (2013)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, vol. 1, pp. 173–180. Association for Computational Linguistics (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Trinity College DublinDublinIreland

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