Dwarakanath, Lokabhiram and Kamsin, Amirrudin and Rasheed, Rasheed Abubakar and Anandhan, Anitha and Shuib, Liyana (2021) Automated machine learning approaches for emergency response and coordination via social media in the aftermath of a disaster: A review. IEEE Access, 9. pp. 68917-68931. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2021.3074819.
Full text not available from this repository.Abstract
Social media communication serves as an integral part of the crisis response following a mass emergency (disaster) event. Regardless of the kind of disaster event, whether it is a hurricane, a flood, an earthquake or a man-made disaster event like a riot or a terrorist attack, social media platforms like Facebook, Twitter etc. have proven to be a powerful facilitator of communication and coordination between disaster victims and other communities. Consequently, several research articles have been published on social media utilization for disaster response. Many of those recent research articles discuss automated machine learning approaches to extract disaster indicating posts, useful for coordination from various social media posts. Despite this, there is a scarcity of comprehensive review of all the major research works pertaining to the utilization of machine learning approaches for disaster response using social media posts. Thus, this study reviews academic research articles in the domain and classifies them across three disaster phase dimensions - early warning and event detection, post-disaster coordination and response, damage assessment. This review would help researchers in choosing further research topics pertaining to automated approaches for actionable information classification and disaster coordination and would help the emergency teams to make well-informed decisions in disaster situations.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Social networking (online); Machine learning; Media; Blogs; Machine learning algorithms; Event detection; Decision making; Disaster communication; machine learning; social media for Emergency coordination; Automated emergency coordination; Crisis informatics; Disaster informatics; Crisis response |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 10 Mar 2022 04:57 |
| Last Modified: | 10 Mar 2022 04:57 |
| URI: | http://eprints.um.edu.my/id/eprint/27851 |
Actions (login required)
 |
View Item |
