Social media postings are increasingly being used in modern days disaster management. Along with the textual information, the contexts and cues inherent in the images posted on social media play an important role in identifying appropriate emergency responses to a particular disaster. In this paper, we proposed a disaster taxonomy of emergency response and used the same taxonomy with an emergency response pipeline together with deep-learning-based image classification and object identification algorithms to automate the emergency response decision-making process. We used the card sorting method to validate the completeness and correctness of the disaster taxonomy. We also used VGG-16 and You Only Look Once (YOLO) algorithms to analyze disaster-related images and identify disaster types and relevant cues (such as objects that appeared in those images). Furthermore, using decision tables and applied analytic hierarchy processes (AHP), we aligned the intermediate outputs to map a disaster-related image into the disaster taxonomy and determine an appropriate type of emergency response for a given disaster. The proposed approach has been validated using Earthquake, Hurricane, and Typhoon as use cases. The results show that 96% of images were categorized correctly on disaster taxonomy using YOLOv4. The accuracy can be further improved using an incremental training approach. Due to the use of cloud-based deep learning algorithms in image analysis, our approach can potentially be useful to real-time crisis management. The algorithms along with the proposed emergency response pipeline can be further enhanced with other spatiotemporal features extracted from multimedia information posted on social media.
In Saudi Arabia, several mitigating measures were implemented in response to the COVID-19 pandemic, including the creation of COVID-19 mobile applications (apps) for public use. The Saudi government has made the use of these apps mandatory for its citizens and residents. However, it is essential to explore the perception that common users have regarding using these apps in terms of usability and user experience. Therefore, this paper assesses user experience in terms of effectiveness, efficiency, and user satisfaction with the usability of the Saudi COVID-19 apps. The reviews of five mobile apps launched by the Saudi Data and AI Authority (SDAIA) and the Ministry of Health in the Apple Store were extracted using an online tool and analyzed using the content analysis method. The number of collected reviews was 29 for Sehha, 406 for Sehhaty, 442 for Mawid, 107 for Tabaud, and 1338 for Tawakkalna. The results of the study showed that Mawid (82%) and Tabaud (81%) had the highest usability of all the apps studied. Sehha (138%) and Sehhaty (−107%) received the lowest usability scores, followed by Tawakkalna (22%). Based on these results, we identified several usability issues with each app. Some of the main problems reported by users were increased battery drain, lack of privacy, and technical issues.
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
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