Assessing wild fire risk in the United States using social media data

Yaojie Yue, Kecui Dong, Xiangwei Zhao, Xinyue Ye

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Massive Geo-tagged social media data provide new opportunities for disaster risk assessment, prevention, and management. This article presents a proof of concept for assessing wildfire risk using Geo-tagged social media data, by taking wildfire risk as a function of wildfire hazard and social–ecological vulnerability. The case study of the United States shows that the regions with the highest wildfire hazard are concentrated in the Western, while the most vulnerable areas are mainly distributed in the Eastern, the Western Coast, and the Southern parts of the nation. Areas with high wildfire risk are mainly located in the Northwestern and Southeastern United States. It shows that the wildfire risk level has significant linear relationship with population density. Massive and vulnerable population might result in significant increase in wildfire risk perception. We conclude that Geo-tagged social media data have great potential in disaster risk studies.

Original languageEnglish (US)
Pages (from-to)972-986
Number of pages15
JournalJournal of Risk Research
Issue number8
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • General Engineering
  • Safety, Risk, Reliability and Quality
  • General Social Sciences
  • Strategy and Management


  • Disaster risk
  • Twitter
  • social–ecological system vulnerability
  • wildfire


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