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For over a decade, social media has proved to be a functional and convenient data source in the Internet of things. Social platforms such as Facebook, Twitter, Instagram, and Reddit have their own styles and purposes. Twitter, among them, has become the most popular platform in the research community due to its nature of attracting people to write brief posts about current and unexpected events (e.g., natural disasters). The immense popularity of such sites has opened a new horizon in `social sensing' to manage disaster response. Sensing through social media platforms can be used to track and analyze natural disasters and evaluate the overall response (e.g., resource allocation, relief, cost and damage estimation). In this paper, we propose a two-step methodology: i) wavelet analysis and ii) predictive modeling to track the progression of a disaster aftermath and predict the time-line. We demonstrate that wavelet features can preserve text semantics and predict the total duration for localized small scale disasters. The experimental results and observations on two real data traces (flash flood in Cummins Falls state park and Arizona swimming hole) showcase that the wavelet features can predict disaster time-line with an error lower than 20% with less than 50% of the data when compared to ground truth.
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This content will become publicly available on April 1, 2026
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.
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- Award ID(s):
- 2346936
- PAR ID:
- 10593026
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 15
- Issue:
- 8
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 4330
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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