Empowering crisis information extraction through actionability event schemata and domain-adaptive pre-training

• Main Authors: ZHANG, Yuhao, LO, Siaw Ling, WIN MYINT, Phyo Yi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: actionability extraction; social media crisis detection; multi-task learning; domain-adaptive pre-training; Databases and Information Systems; Numerical Analysis and Scientific Computing; Social Media
• Online Access: https://ink.library.smu.edu.sg/sis_research/9721
• Institution: Singapore Management University

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.