Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2023
Abstract
Social media has become a valuable information source for crisis informatics. While various methods were proposed to extract relevant information during a crisis, their adoption by field practitioners remains low. In recent fieldwork, actionable information was identified as the primary information need for crisis responders and a key component in bridging the significant gap in existing crisis management tools. In this paper, we proposed a Crisis Actionability Extraction System for filtering, classification, phrase extraction, severity estimation, localization, and aggregation of actionable information altogether. We examined the effectiveness of transformer-based LSTM-CRF architecture in Twitter-related sequence tagging tasks and simultaneously extracted actionable information such as situational details and crisis impact via Multi-Task Learning. We demonstrated the system’s practical value in a case study of a real-world crisis and showed its effectiveness in aiding crisis responders with making well-informed decisions, mitigating risks, and navigating the complexities of the crisis.
Keywords
Actionability, Crisis response, Multi-Task Learning
Discipline
Artificial Intelligence and Robotics | Social Media
Research Areas
Data Science and Engineering
Publication
Proceedings of International Conference on Information Systems 2023, Hyderabad, India, December 10-13
Publisher
Association for Information Systems
City or Country
Atlanta, GA, United States of America
Citation
ZHANG, Yuhao; LO, Siaw Ling; and WIN MYINT, Phyo Yi.
Transformer-based Multi-Task Learning for crisis actionability extraction. (2023). Proceedings of International Conference on Information Systems 2023, Hyderabad, India, December 10-13.
Available at: https://ink.library.smu.edu.sg/sis_research/8517
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.