1.1
1
xml
info:srw/schema/1/mods-v3.2
Predicting Hurricane Damage Using Social Media Posts Coupled with Physical and Socio-Economic Variables
Guoqin Ma
author
Chittayong Surakitbanharn
author
2019
Iscram
Valencia, Spain
English
During a natural disaster or emergency event, individual social media posts or hot spots may not necessarily correlate
to the most devastated areas. To better understand the correlation between social media and physical damage, we
compare Tweets, data about the physical environment, and socio-economic factors with insurance claim information
(as a proxy for physical damage) from 2017 Hurricane Irma in the state of Florida. We use machine learning
to identify relevant Tweets, sensitivity analyses to identify socio-economic factors, and statistical regression to
determine the predictive capability of insurance claims as a proxy for damage. We find that Tweets alone result in a
poorly fitted regression model of insurance claims, but the inclusion of physical features (e.g., power outages, wind
level) and socio-economic factors (e.g., population density, education, Internet access) improves the model?s fit.
Such models contribute to the knowledge base that may allow social media to predict damage in real-time.
Social media
disaster management
damage prediction
exported from refbase (http://idl.iscram.org/show.php?record=1955), last updated on Fri, 22 Nov 2019 12:01:08 +0100
text
http://idl.iscram.org/files/guoqinma/2019/1955_GuoqinMa+ChittayongSurakitbanharn2019.pdf
GuoqinMa+ChittayongSurakitbanharn2019
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management
Iscram 2019
Franco
Z
editor
González
J
J
editor
Canós
J
H
editor
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
2019
Iscram
Valencia, Spain
conference publication
978-84-09-10498-7
2411-3387
1