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

Chao He · Da Hu

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Public Relations and Crisis Communication Disaster Management and Resilience Sentiment Analysis and Opinion Mining

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.

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2018CrisisMMD: Multimodal Twitter Datasets from Natural Disasters 2015Who Tweets with Their Location? Understanding the Relationship between Demographic Characteristics and the Use of Geoservices and Geotagging on Twitter 2013Practical extraction of disaster-relevant information from social media 2016Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages 2019The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects 2017Robust Classification of Crisis-Related Data on Social Networks Using Convolutional Neural Networks 2021HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks 2020Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response

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