Social Media-Based Intelligence for Disaster Response and Management in Smart Cities

Khatoon, Shaheen; Asif, Amna; Hasan, Md. Maruf; Alshamari, Majed

doi:10.1007/978-3-030-84459-2_11

Cited by 11 publications

(14 citation statements)

References 30 publications

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“…Many studies highlight the usefulness of technologies for EDMPP. To focus on a few technologies; various studies describe the utility of artificial intelligence and machine learning and reasoning [69,71,80,168,169,[193][194][195][196][197][198][199][200][201][202][203][204][205][206][207][208][209][210] for EDMPP including for the evaluation of societal implications [211]. If it is seen as useful to evaluate societal implications, the data must contain high quality information on the 'social', especially in relation to marginalized groups.…”

Section: Edmpp Marginalized Groups Edi and Technologymentioning

confidence: 99%

Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review

Lillywhite

Wolbring

2022

Sustainability

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The importance of emergency and disaster management, preparedness, and planning (EDMPP) is ever increasing with COVID-19 being one recent EDMPP challenge. EDMPP is impacted by and impacts the ‘social’ of individuals and societies. Therefore, a thorough understanding of the ‘social’ is important for providing EDMPP. Marginalized populations are over-proportionally impacted by emergencies and disasters and often overlooked in EDMPP. Therefore, it is especially important to understand the lived experience of marginalized groups and to involve marginalized groups in providing knowledge for EDMPP. Technologies such as artificial intelligence and machine learning and reasoning, e-coaching, other decision support systems and Bayesian belief networks are increasingly employed for EDMPP. However, biases and other problems in the use of technologies for EDMPP are noted. Understanding the ‘social’ of marginalized populations and others is essential for designing algorithms and other technologies that are not biased towards marginalized populations and others. The phrase “equity, diversity, and inclusion” (EDI), other EDI linked phrases, and EDI frameworks are increasingly employed in workplaces to improve research, education, and workplace environments for marginalized groups such as women, Indigenous Peoples, visible minorities, racialized minorities, disabled people, people with disabilities and LGBTQ2S+. EDMPP actors are workers. Using EDI in EDMPP could improve the EDMPP situation of marginalized groups by encouraging knowledge production by and about marginalized groups related to EDMPP. The main objective of this study was to map out the engagement with the ‘social’, EDI and marginalized groups in the EDMPP-focused academic literature in general and the EDMPP academic literature covering disabled people, patients, technologies and COVID-19. A scoping review using the academic databases SCOPUS, Web of Science, and the databases accessible under Compendex and EBSCO-HOST were employed to fulfill the objectives. The study found little coverage of marginalized populations and EDI phrases and frameworks, and a lack of many terms linked to the ‘social’ in the literature searched. These gaps need to be filled given the importance of EDMPP to the ‘social’ of individuals and societies.

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Section: Edmpp Marginalized Groups Edi and Technologymentioning

confidence: 99%

Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review

Lillywhite

Wolbring

2022

Sustainability

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“…Moreover, comprehensive semantic analysis of the multimodal and multilingual data collected in the previous step, through applying artificial intelligence techniques and mapping it with disaster ontology, is presented by the black box in Figure 1c. Efficient representation of the crisis information on the interactive user dashboard for incident-monitoring and visualization to facilitate a prompt emergency response is shown in Figure 1d [8]. The process of data collection and preprocessing involves all steps to prepare it for machine learning and deep learning models of crisis event detection and classification.…”

Section: Disaster Management Framework and Need For Information Visua...mentioning

confidence: 99%

“…x FOR PEER REVIEW 4 of 24 information on the interactive user dashboard for incident-monitoring and visualization to facilitate a prompt emergency response is shown in Figure 1 (d) [8]. The process of data collection and preprocessing involves all steps to prepare it for machine learning and deep learning models of crisis event detection and classification.…”

Section: Disaster Management Framework and Need For Information Visua...mentioning

confidence: 99%

“…In the past, several studies have been conducted on capturing useful disaster-related information from social media text [6], such as disaster topic classification [7][8][9][10], audio-video event detection [8], image detection, and classification algorithms for image-based crisis event detection [11][12][13]. The previous studies are interesting in that the social media data (text, images, audio, and video) were not only used to detect the disaster, but they were further preprocessed and modeled to classify the severity and nature of the event.…”

Section: Introduction 1disaster Management Systemsmentioning

confidence: 99%

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Designing Multimodal Interactive Dashboard of Disaster Management Systems

AlAbdulaali

Asif

Khatoon

et al. 2022

Sensors

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Disasters and crises are inevitable in this world. In the aftermath of a disaster, a society’s overall growth, resources, and economy are greatly affected as they cause damages from minor to huge proportions. Around the world, countries are interested in improving their emergency decision-making. The institutions are paying attention to collecting different types of data related to crisis information from various resources, including social media, to improve their emergency response. Previous efforts have focused on collecting, extracting, and classifying crisis data from text, audio, video, or files; however, the development of user-friendly multimodal disaster data dashboards to support human-to-system interactions during an emergency response has received little attention. Our paper seeks to fill this gap by proposing usable designs of interactive dashboards to present multimodal disaster information. For this purpose, we first investigated social media data and metadata for the required elicitation and analysis purposes. These requirements are then used to develop interactive multimodal dashboards to present complex disaster information in a usable manner. To validate our multimodal dashboard designs, we have conducted a heuristic evaluation. Experts have evaluated the interactive disaster dashboards using a customized set of heuristics. The overall assessment showed positive feedback from the evaluators. The proposed interactive multimodal dashboards complement the existing techniques of collecting textual, image, audio, and video emergency information and their classifications for usable presentation. The contribution will help the emergency response personnel in terms of useful information and observations for prompt responses to avoid significant damage.

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“…The capacity of Twitter in terms of event detection was also studied widely, and various techniques were proposed to detect events from tweets [4,5]. Researchers in this domain have proposed systems for news recommendations [6,7], sentiment analysis [8][9][10], disaster and emergency alerts [11], response systems [12,13], etc. Analyzing such data is essential for event detection, especially in emergency conditions due to natural disasters such as floods, hurricanes, fires, or earthquakes [1,14].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Eyewitness Messages for Disaster Events Using Linguistic Rules and ML/AI Approaches

et al. 2022

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Emergency response systems require precise and accurate information about an incident to respond accordingly. An eyewitness report is one of the sources of such information. The research community has proposed diverse techniques to identify eyewitness messages from social media platforms. In our previous work, we created grammar rules by exploiting the language structure, linguistics, and word relations to automatically extract feature words to classify eyewitness messages for different disaster types. Our previous work adopted a manual classification technique and secured the maximum F-Score of 0.81, far less than the static dictionary-based approach with an F-Score of 0.92. In this work, we enhanced our work by adding more features and fine-tuning the Linguistic Rules to identify feature words related to Twitter Eyewitness messages for Disaster events, named as LR-TED approach. We used linguistic characteristics and labeled datasets to train several machine learning and deep learning classifiers for classifying eyewitness messages and secured a maximum F-score of 0.93. The proposed LR-TED can process millions of tweets in real-time and is scalable to diverse events and unseen content. In contrast, the static dictionary-based approaches require domain experts to create dictionaries of related words for all the identified features and disaster types. Additionally, LR-TED can be evaluated on different social media platforms to identify eyewitness reports for various disaster types in the future.

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Social Media-Based Intelligence for Disaster Response and Management in Smart Cities

Cited by 11 publications

References 30 publications

Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review

Emergency and Disaster Management, Preparedness, and Planning (EDMPP) and the ‘Social’: A Scoping Review

Designing Multimodal Interactive Dashboard of Disaster Management Systems

Automatic Classification of Eyewitness Messages for Disaster Events Using Linguistic Rules and ML/AI Approaches

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