A Framework for Promoting the Experience of Novices in Examining Articles that Alert Dangers of Disaster on Social Media
- Resource Type
- Conference
- Authors
- Ishii, Tomoki; Nakayama, Hiroki; Onuma, Ryo; Kaminaga, Hiroaki; Miyadera, Youzou; Nakamura, Shoichi
- Source
- 2022 International Conference on Computational Science and Computational Intelligence (CSCI) CSCI Computational Science and Computational Intelligence (CSCI), 2022 International Conference on. :2081-2085 Dec, 2022
- Subject
- Computing and Processing
Landslides
Rain
Social networking (online)
Scientific computing
Blogs
Media
Data mining
Disaster Alerts
Action for Reducing Disaster Damage
Experience of Examining Articles
Social Media
- Language
- ISSN
- 2769-5654
When natural disasters such as heavy rains and landslides occur, there are an increasing number of cases where articles that appeal to imminent dangers and the need to evacuate (called disaster alerts) are posted by individuals on social media to help people reduce damages. Although such articles should be put to the best use, it is difficult for inexperienced people to discover valuable articles from a huge number of posts on social media. In this paper, we mainly describe a framework for promoting the experience of students in examining disaster alerts and methods for extracting noteworthy article candidates. We also describe an experiment using actual Twitter posts and discuss the characteristics of our methods on the basis of its results.