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Crisis Mapping During Natural Disasters via Text Analysis of Social Media Messages

  • Stefano Cresci
  • Andrea Cimino
  • Felice Dell’Orletta
  • Maurizio Tesconi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9419)

Abstract

Recent disasters demonstrated the central role of social media during emergencies thus motivating the exploitation of such data for crisis mapping. We propose a crisis mapping system that addresses limitations of current state-of-the-art approaches by analyzing the textual content of disaster reports from a twofold perspective. A damage detection component employs a SVM classifier to detect mentions of damage among emergency reports. A novel geoparsing technique is proposed and used to perform message geolocation. We report on a case study to show how the information extracted through damage detection and message geolocation can be combined to produce accurate crisis maps. Our crisis maps clearly detect both highly and lightly damaged areas, thus opening up the possibility to prioritize rescue efforts where they are most needed.

Keywords

Twitter Social media mining Emergency management Crisis mapping Geoparsing 

Notes

Acknowledgements

The authors would like to thank Matteo Abrate and Salvatore Rinzivillo, for their insightful suggestions about data visualization. This research was supported by the .it domain registration authority (Registro .it) funded project SoS - Social Sensing (http://socialsensing.it/en).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stefano Cresci
    • 1
    • 2
  • Andrea Cimino
    • 3
  • Felice Dell’Orletta
    • 3
  • Maurizio Tesconi
    • 2
  1. 1.Bell LabsAlcatel-LucentParisFrance
  2. 2.Institute for Informatics and Telematics, IIT-CNRPisaItaly
  3. 3.Institute for Computational Linguistics, ILC-CNRPisaItaly

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