Curd: Context-Aware Relevance and Urgency Determination

Abstract

During emergencies where time is of the essence, efficient management of disasters depends on swiftly recognizing relevant and urgent information from online platforms like X (Twitter), which is imperative for augmenting established response frameworks, such as the 911 emergency system. This paper introduces CURD, a Context-aware Relevance and Urgency Determination system designed to enhance the efficiency of disaster response. The system addresses two critical challenges: filtering out irrelevant data and assessing the urgency of relevant information. Our approach includes a multi-level annotation process for event type, relevancy, and an urgency annotation algorithm that significantly improves information extraction accuracy and efficiency. CURDdl, our classifier, uses a deep learning pipeline architecture with a combination of transformer models, a convolution layer, and custom attention mechanisms to classify disaster-related tweets into multiclass-event type, binary-relevance-and-urgency categories, and rank urgent ones based on significance. Experimental results show that our best baseline classifiers for all three tasks achieved ≥ 88% F1 and accuracy, and ≥ 94%. AUC. Our models also outperformed models from related works in all metrics, validating the effectiveness of CURD in prioritizing response messages that will facilitate decision-making and resource allocation in disaster scenarios. CURD annotated dataset and code are available on GitHub.

Department(s)

Computer Science

Comments

Missouri University of Science and Technology, Grant None

Keywords and Phrases

Data Annotation; Emergency Management; Relevancy; Social Media; Urgency

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Association for Computing Machinery, All rights reserved.

Publication Date

10 Jul 2024

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