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 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.