Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/8/4330 |
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| author | Chao He Da Hu |
| author_facet | Chao He Da Hu |
| author_sort | Chao He |
| collection | DOAJ |
| description | Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system’s dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model’s effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters. |
| format | Article |
| id | doaj-art-fe93a62edb794c0686abb9d539c4544a |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-fe93a62edb794c0686abb9d539c4544a2025-08-20T03:14:21ZengMDPI AGApplied Sciences2076-34172025-04-01158433010.3390/app15084330Social Media Analytics for Disaster Response: Classification and Geospatial Visualization FrameworkChao He0Da Hu1Department of Civil and Environmental Engineering, Kennesaw State University, Marietta, GA 30060, USADepartment of Civil and Environmental Engineering, Kennesaw State University, Marietta, GA 30060, USASocial media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system’s dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model’s effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters.https://www.mdpi.com/2076-3417/15/8/4330deep learningdisaster responseinteractive mapsocial mediatweet |
| spellingShingle | Chao He Da Hu Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework Applied Sciences deep learning disaster response interactive map social media tweet |
| title | Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework |
| title_full | Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework |
| title_fullStr | Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework |
| title_full_unstemmed | Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework |
| title_short | Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework |
| title_sort | social media analytics for disaster response classification and geospatial visualization framework |
| topic | deep learning disaster response interactive map social media tweet |
| url | https://www.mdpi.com/2076-3417/15/8/4330 |
| work_keys_str_mv | AT chaohe socialmediaanalyticsfordisasterresponseclassificationandgeospatialvisualizationframework AT dahu socialmediaanalyticsfordisasterresponseclassificationandgeospatialvisualizationframework |