Mining Twitter Data for Improved Understanding of Disaster Resilience

Lei Zou, Nina S. N. Lam, Heng Cai and Yi Qiang

Annals of the American Association of Geographers, 2018, vol. 108, issue 5, 1422-1441

Abstract: Coastal communities faced with multiple hazards have shown uneven responses and behaviors. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into the three phases of the emergency management: preparedness, response, and recovery. This study analyzes the spatial–temporal patterns of Twitter activities during Hurricane Sandy, which struck the U.S. Northeast on 29 October 2012. The study area includes 126 counties affected by Hurricane Sandy. The objectives are threefold: (1) to derive a set of common indexes from Twitter data so that they can be used for emergency management and resilience analysis; (2) to examine whether there are significant geographical and social disparities in disaster-related Twitter use; and (3) to test whether Twitter data can improve postdisaster damage estimation. Three corresponding hypotheses were tested. Results show that common indexes derived from Twitter data, including ratio, normalized ratio, and sentiment, could enable comparison across regions and events and should be documented. Social and geographical disparities in Twitter use existed in the Hurricane Sandy event, with higher disaster-related Twitter use communities generally being communities of higher socioeconomic status. Finally, adding Twitter indexes into a damage estimation model improved the adjusted R2 from 0.46 to 0.56, indicating that social media data could help improve postdisaster damage estimation, but other environmental and socioeconomic variables influencing the capacity to reducing damage might need to be included. The knowledge gained from this study could provide valuable insights into strategies for utilizing social media data to increase resilience to disasters.

Date: 2018
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DOI: 10.1080/24694452.2017.1421897

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