Citation

Yuan F, Liu R. Int. J. Disaster Risk Reduct. 2018; 28: 758-767.

Copyright

(Copyright © 2018, Elsevier Publishing)

DOI

10.1016/j.ijdrr.2018.02.003

PMID

unavailable

Abstract

The rapid damage assessment plays a critical role in crisis management. Collection of timely information for rapid damage assessment is particularly challenging during natural disasters. Remote sensing technologies were used for data collection during disasters. However, due to the large areas affected by major disasters such as Hurricane Matthew, specific data cannot be collected in time such as the location information. Social media can serve as a crowdsourcing platform for citizens' communication and information sharing during natural disasters and provide the timely data for identifying affected areas to support rapid damage assessment during disasters. Nevertheless, there is very limited existing research on the utility of social media data in damage assessment. Even though some investigation of the relationship between social media activities and damages was conducted, the employment of damage-related social media data in exploring the fore-mentioned relationship remains blank. This paper, for the first time, establishes the index dictionary by semantic analysis for the identification of damage-related tweets posted during Hurricane Matthew in Florida. Meanwhile, the insurance claim data from the publication of Florida Office of Insurance Regulation is used as a representative of real hurricane damage data in Florida. This study performs a correlation analysis and a comparative analysis of the geographic distribution of social media data and damage data at the county level in Florida. We find that employing social media data to identify critical affected areas at the county level during disasters is viable. Damage data has a closer relationship with damage-related tweets than disaster-related tweets.

Language: en

Keywords

Social media; Data mining; Crowdsourcing; Case study; Damage assessment; Semantic analysis