Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2025
Abstract
One of the persistent challenges in crisis detection is inferring actionable information to support emergency response. Existing methods focus on situational awareness but often lack actionable insights. This study proposes a holistic approach to implementing an actionability extraction system on social media, including requirement gathering, selection of machine learning tasks, data preparation, and integration with existing resources, providing guidance for governments, civil services, emergency workers, and researchers on supplementing existing channels with actionable information from social media. Our solution leverages an actionability schema and domain-adaptive pre-training, improving upon the state-of-the-art model by 5.5% and 10.1% in micro and macro F1 scores.
Keywords
actionability extraction, social media crisis detection, multi-task learning, domain-adaptive pre-training
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Research Areas
Data Science and Engineering
Publication
Information & Management
Volume
62
Issue
1
First Page
1
Last Page
25
ISSN
0378-7206
Identifier
10.1016/j.im.2024.104065
Publisher
Elsevier
Citation
ZHANG, Yuhao; LO, Siaw Ling; and WIN MYINT, Phyo Yi.
Empowering crisis information extraction through actionability event schemata and domain-adaptive pre-training. (2025). Information & Management. 62, (1), 1-25.
Available at: https://ink.library.smu.edu.sg/sis_research/9721
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.im.2024.104065
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons, Social Media Commons