Journals / CMC / Vol.68, No.3
Table of Content

Research Article

BEST PAPER 2021

Development of Social Media Analytics System for Emergency Event Detection and Crisis Management

Shaheen Khatoon1,*, Majed A. Alshamari1, Amna Asif1, Md Maruf Hasan1, Sherif Abdou2, Khaled Mostafa Elsayed3, Mohsen Rashwan4
1 Department of Information System, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
2 Faculty of Computers and Artificial Intelligence, Cairo University, Egypt
3 Faculty of Computing & Information, Cairo University, Egypt
4 Faculty of Engineering, Cairo University, Egypt
* Corresponding Author: Shaheen Khatoon. Email:
(This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Abstract

Social media platforms have proven to be effective for information gathering during emergency events caused by natural or human-made disasters. Emergency response authorities, law enforcement agencies, and the public can use this information to gain situational awareness and improve disaster response. In case of emergencies, rapid responses are needed to address victims’ requests for help. The research community has developed many social media platforms and used them effectively for emergency response and coordination in the past. However, most of the present deployments of platforms in crisis management are not automated, and their operational success largely depends on experts who analyze the information manually and coordinate with relevant humanitarian agencies or law enforcement authorities to initiate emergency response operations. The seamless integration of automatically identifying types of urgent needs from millions of posts and delivery of relevant information to the appropriate agency for timely response has become essential. This research project aims to develop a generalized Information Technology (IT) solution for emergency response and disaster management by integrating social media data as its core component. In this paper, we focused on text analysis techniques which can help the emergency response authorities to filter through the sheer amount of information gathered automatically for supporting their relief efforts. More specifically, we applied state-of-the-art Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL) techniques ranging from unsupervised to supervised learning for an in-depth analysis of social media data for the purpose of extracting real-time information on a critical event to facilitate emergency response in a crisis. As a proof of concept, a case study on the COVID-19 pandemic on the data collected from Twitter is presented, providing evidence that the scientific and operational goals have been achieved.

Keywords

Crisis management; social media analytics; machine learning; natural language processing; deep learning
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