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Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event

Author

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  • Li, Lingyao
  • Bensi, Michelle
  • Cui, Qingbin
  • Baecher, Gregory B.
  • Huang, You

Abstract

Rapid appraisal of damages related to hazard events is of importance to first responders, government agencies, insurance industries, and other private and public organizations. While satellite monitoring, ground-based sensor systems, inspections and other technologies provide data to inform post-disaster response, crowdsourcing through social media is an additional and novel data source. In this study, the use of social media data, principally Twitter postings, is investigated to make approximate but rapid early assessments of damages following a disaster. The goal is to explore the potential utility of using social media data for rapid damage assessment after sudden-onset hazard events and to identify insights related to potential challenges. This study defines a text-based damage assessment scale for earthquake damages, and then develops a text classification model for rapid damage assessment. Although the accuracy remains a challenge compared to ground-based instrumental readings and inspections, the proposed damage assessment model features rapidity with large amounts of data at spatial densities that exceed those of conventional sensor networks. The 2019 Ridgecrest, California earthquake sequence is investigated as a case study.

Suggested Citation

  • Li, Lingyao & Bensi, Michelle & Cui, Qingbin & Baecher, Gregory B. & Huang, You, 2021. "Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event," International Journal of Information Management, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:ininma:v:60:y:2021:i:c:s0268401221000712
    DOI: 10.1016/j.ijinfomgt.2021.102378
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    Cited by:

    1. Songhua Hu & Kailai Wang & Lingyao Li & Yingrui Zhao & Zhenbing He & Yunpeng & Zhang, 2023. "Modeling Link-level Road Traffic Resilience to Extreme Weather Events Using Crowdsourced Data," Papers 2310.14380, arXiv.org.

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