{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T07:55:31Z","timestamp":1784620531857,"version":"3.55.0"},"reference-count":42,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T00:00:00Z","timestamp":1676592000000},"content-version":"unspecified","delay-in-days":47,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Disaster med. public health prep."],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec id=\"S1935789322002853_as1\"><jats:title>Objective:<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"S1935789322002853_as2\"><jats:title>Methods:<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec id=\"S1935789322002853_as3\"><jats:title>Results:<\/jats:title><jats:p>Users residing in counties affected by Hurricane Matthew posted tweets related to preparedness (<jats:italic>n<\/jats:italic>= 171; 16%), awareness (<jats:italic>n<\/jats:italic>= 407; 38%), call-for-action or help (<jats:italic>n<\/jats:italic>= 206; 19%), and evacuations (<jats:italic>n<\/jats:italic>= 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).<\/jats:p><\/jats:sec><jats:sec id=\"S1935789322002853_as4\"><jats:title>Conclusions:<\/jats:title><jats:p>Social media data can be used to detect and evaluate damages of communities affected by natural disasters and identify users\u2019 needs in at-risk areas before the event takes place to aid during the preparedness phases.<\/jats:p><\/jats:sec>","DOI":"10.1017\/dmp.2022.285","type":"journal-article","created":{"date-parts":[[2023,2,17]],"date-time":"2023-02-17T14:26:45Z","timestamp":1676644005000},"source":"Crossref","is-referenced-by-count":3,"title":["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"],"prefix":"10.1017","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4786-9438","authenticated-orcid":false,"given":"Kamalich","family":"Muniz-Rodriguez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2056-0182","authenticated-orcid":false,"given":"Jessica S.","family":"Schwind","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerardo","family":"Chowell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5496-2529","authenticated-orcid":false,"given":"Isaac Chun-Hai","family":"Fung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2023,2,17]]},"reference":[{"key":"S1935789322002853_ref27","unstructured":"27. 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