TY - JOUR
T1 - Social media data and post-disaster recovery
AU - Jamali, Mehdi
AU - Nejat, Ali
AU - Ghosh, Souparno
AU - Jin, Fang
AU - Cao, Guofeng
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/2
Y1 - 2019/2
N2 - This study introduces a multi-step methodology for analyzing social media data during the post-disaster recovery phase of Hurricane Sandy. Its outputs include identification of the people who experienced the disaster, estimates of their physical location, assessments of the topics they discussed post-disaster, analysis of the tract-level relationships between the topics people discussed and tract-level internal attributes, and a comparison of these outputs to those of people who did not experience the disaster. Faith-based, community, assets, and financial topics emerged as major topics of discussion within the context of the disaster experience. The differences between predictors of these topics compared to those of people who did not experience the disaster were investigated in depth, revealing considerable differences among vulnerable populations. The use of this methodology as a new Machine Learning Algorithm to analyze large volumes of social media data is advocated in the conclusion.
AB - This study introduces a multi-step methodology for analyzing social media data during the post-disaster recovery phase of Hurricane Sandy. Its outputs include identification of the people who experienced the disaster, estimates of their physical location, assessments of the topics they discussed post-disaster, analysis of the tract-level relationships between the topics people discussed and tract-level internal attributes, and a comparison of these outputs to those of people who did not experience the disaster. Faith-based, community, assets, and financial topics emerged as major topics of discussion within the context of the disaster experience. The differences between predictors of these topics compared to those of people who did not experience the disaster were investigated in depth, revealing considerable differences among vulnerable populations. The use of this methodology as a new Machine Learning Algorithm to analyze large volumes of social media data is advocated in the conclusion.
KW - Post-disaster recovery
KW - Social media
KW - Temporal–spatial patterns
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85053812692&partnerID=8YFLogxK
U2 - 10.1016/j.ijinfomgt.2018.09.005
DO - 10.1016/j.ijinfomgt.2018.09.005
M3 - Article
AN - SCOPUS:85053812692
VL - 44
SP - 25
EP - 37
JO - International Journal of Information Management
JF - International Journal of Information Management
SN - 0268-4012
ER -