Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Public Relations and Crisis Communication
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Civil and Environmental Engineering, Kennesaw State University, Marietta, GA 30060, USA
Funding
US National Science Foundation (NSF)
References (28)
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