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

Publication Type

Journal Article

Publication Date

11-2024

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

ISSN

0378-7206

Identifier

10.1016/j.im.2024.104065

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.im.2024.104065

This document is currently not available here.

Share

COinS