A Tutorial on Social Media Data Analytics for Disaster Management

Abstract

A disaster can refer to an effect and result of natural hazards like the hurricane, flood, earthquake, tornado, heatwave, etc. Every activity of a disaster management such as taking precautions, managing evacuation, running rescue missions demands accurate and up-to-date information to allow a quick, easy and cost-effective process and hence reduce the loss of lives and properties. Social media has emerged as a valuable supplementary tool in this context, providing real-time data that can assist authorities in developing prompt and effective response strategies. However, despite its potential, utilizing social media data for disaster management presents several challenges. It needs a multi-faceted approach that leverages deep learning and natural language processing (NLP) techniques tackling the complexities of contextual information and the relevance of social media content. The tutorial will offer actionable insights that significantly enhance situational awareness information, decision-making, and resource allocation during disasters. The tutorial will focus on i) How can we detect, classify, and analyze hate and offensive emotions during large-scale events based on the social media data such as tweets? ii) How can deep learning models improve sentiment analysis by identifying low-level emotions in major events? iii) How can fine-grained data enhance crisis communication classification and decision-making in disaster response? iv) How can we assess information relevance and urgency to prioritize emergency responses? v) How can we identify and classify first responders during emergencies? vi) How can we develop an automated fact-checking system to verify disaster claims and combat misinformation? vii) How can unsupervised learning be used to extract key phrases and detect critical sub-events from unstructured disaster data?

Department(s)

Computer Science

Comments

National Science Foundation, Grant None

Keywords and Phrases

Disaster; NLP; Social Media

International Standard Book Number (ISBN)

978-303197206-5

International Standard Serial Number (ISSN)

1611-3349; 0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2026 Springer, All rights reserved.

Publication Date

01 Jan 2026

Link to Full Text

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