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Author (down) Guoqin Ma; Chittayong Surakitbanharn
Title Predicting Hurricane Damage Using Social Media Posts Coupled with Physical and Socio-Economic Variables Type Conference Article
Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019
Volume Issue Pages
Keywords Social media, disaster management, damage prediction
Abstract 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.
Address Stanford University, United States of America
Corporate Author Thesis
Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium
Track T8- Social Media in Crises and Conflicts Expedition Conference 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
Notes Approved no
Call Number Serial 1955
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