Work

Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework

Year: 2025

Type: article

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: Applied Sciences

DOI: 10.3390/app15084330

Authors: Chao He, Da Hu

Institution: Kennesaw State University

Language: English

Topic: Public Relations and Crisis Communication

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896