Rapid Disaster Response and Damage Estimation with Social Media and Pretrained Large Language Models: Insights from Multiple Hurricanes.
Based on social media's potential in understanding and fortifying situational awareness, these platforms have been widely used in disaster-related research and practice. The accuracy of mining critical, actionable information from social media needs enhancement, however, to support real-time disaste...
| Publicado en: | Annals of the American Association of Geographers Vol. 116; no. 3; pp. 501 - 524 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
2026
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=191615428&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 191615428 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 24694452 JRMH jtl: Annals of the American Association of Geographers issn: 24694452 maglogo: N pubinfo: dt: 2026 vid: 116 iid: 3 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 191615428 10.1080/24694452.2025.2560491 ppf: 501 ppct: 23 formats: tig: atl: Rapid Disaster Response and Damage Estimation with Social Media and Pretrained Large Language Models: Insights from Multiple Hurricanes. aug: au: Zhou, Bing Zou, Lei Yang, Mingzheng Lin, Binbin Mandal, Debayan Abedin, Joynal Cai, Heng Ji, Shuiwang Klein, Andrew Tian, Hao affil: Department of Geography and Sustainability, University of Tennessee, Knoxville, USA Department of Geography, Pennsylvania State University, USA Department of Geography, Texas A&M University, USA Department of Computer Science and Engineering, Texas A&M University, USA su: Social media Situational awareness Hurricanes Emergency management Language models Real-time computing Floods sug: subj: Social media Situational awareness Hurricanes Emergency management Emergency and Other Relief Services Other federal protective services Other provincial protective services Other municipal protective services Other Justice, Public Order, and Safety Activities Language models Real-time computing Floods ab: Based on social media's potential in understanding and fortifying situational awareness, these platforms have been widely used in disaster-related research and practice. The accuracy of mining critical, actionable information from social media needs enhancement, however, to support real-time disaster management. This study demonstrates a novel framework that leverages pretrained large language models to accurately parse fine-grained, location-based information from X (formerly Twitter) for real-time disaster response. The framework categorizes and locates messages into four classes with actionable information: human requesting rescue, animal needing help, infrastructural damage, and shelter information. The performance of the framework is manually validated and proven reliable through random sampling. A comparative study is conducted with data collected from Hurricanes Harvey (2017), Irma (2017), and Ian (2022). Spatiotemporal analysis reveals that social media indexes can be an additional source for predicting damage, especially for the hurricanes that cause more indirect damage such as flood inundation than direct wind damage. Knowledge derived from social media data analysis can assist policymakers, first responders, and volunteers in supporting future disaster responses. The research proves the potential of social media and advocates for improved data access in a privacy-conscious manner, reinforcing social media's role in disaster response. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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