Automated machine learning approaches for emergency response and coordination via social media in the aftermath of a disaster: A review

• Main Authors: Dwarakanath, Lokabhiram, Kamsin, Amirrudin, Rasheed, Rasheed Abubakar, Anandhan, Anitha, Shuib, Liyana
• Format: Article
• Published: Institute of Electrical and Electronics Engineers 2021
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.um.edu.my/27851/

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.