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Exploring Social Media Network Connections to Assist During Public Health Emergency Response: A Retrospective Case-Study of Hurricane Matthew and Twitter Users in Georgia, USA

Published online by Cambridge University Press:  17 February 2023

Kamalich Muniz-Rodriguez*
Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA Ponce Research Institute, Ponce Medical School Foundation, Ponce, Puerto Rico
Jessica S. Schwind
Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA
Jingjing Yin
Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA
Hai Liang
School of Journalism and Communication, The Chinese University of Hong Kong, Hong Kong
Gerardo Chowell
Department of Population Health Sciences, Georgia State University, Atlanta, GA, USA
Isaac Chun-Hai Fung
Department of Biostatistics, Epidemiology, and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA
Corresponding author: Kamalich Muniz-Rodriguez, Email:



To assist communities who suffered from hurricane-inflicted damages, emergency responders may monitor social media messages. We present a case-study using the event of Hurricane Matthew to analyze the results of an imputation method for the location of Twitter users who follow school and school districts in Georgia, USA.


Tweets related to Hurricane Matthew were analyzed by content analysis with latent Dirichlet allocation models and sentiment analysis to identify needs and sentiment changes over time. A hurdle regression model was applied to study the association between retweet frequency and content analysis topics.


Users residing in counties affected by Hurricane Matthew posted tweets related to preparedness (n = 171; 16%), awareness (n = 407; 38%), call-for-action or help (n = 206; 19%), and evacuations (n = 93; 9%), with mostly a negative sentiment during the preparedness and response phase. Tweets posted in the hurricane path during the preparedness and response phase were less likely to be retweeted than those outside the path (adjusted odds ratio: 0.95; 95% confidence interval: 0.75, 1.19).


Social media data can be used to detect and evaluate damages of communities affected by natural disasters and identify users’ needs in at-risk areas before the event takes place to aid during the preparedness phases.

Original Research
© The Author(s), 2023. Published by Cambridge University Press on behalf of Society for Disaster Medicine and Public Health, Inc.

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