Social Media Support Mechanisms for Organizational Adaptation in Governmental Response to Extreme Natural Hazards

Zhang, Haibo; Chen, Wu

doi:10.1061/(asce)nh.1527-6996.0000555

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…The proposed services include event detection and selective information retrieval from online platforms, enhancing meteorological models and situational awareness. In the 2016 Funing volcano of China (Zhang and Chen 2022), social media supported the execution of essential tasks and improved overall government adaptation to the disaster, showcasing the valuable role of social media in enhancing governmental response to extreme natural hazards.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Models for Mining Social Media Data for Effective Natural Disaster Assessment

Mittal,

Karki,

Parasher

et al. 2023

Preprint

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Satellite technology has emerged as a key tool for effective management and assessment of natural disasters. However, the challenge of accurately estimating impacted populations and assessing building damage, often obscured from aerial views, persists. To address this, the integration of imagery and textual data from social networks offers a promising solution. This study employs Twitter and Flickr datasets, using SVM, CNN, XGBoost, Logistic Regression, and Gradient Boost to extract insights. The sentiment analysis component categorizes disaster-affected individuals' emotions as panic, neutral, or non-panic. The Logistic Regression model excels in text classification, boasting an impressive 88.99% accuracy on the test dataset and 83.45% on training. The framework introduces an Aid analysis model, which gives us an impressive accuracy of 83.16% for the classification of tweets based on the aid sought by people through tweets. Image classification employs CNN, achieving 83.29% accuracy to comprehend disaster impact visually. Given real-time social media responses, the system assists government and organisations promptly, prioritising assistance. It serves as a dependable aid resource, enabling efficient responses tailored to affected communities. Thus, this approach holds potential to significantly enhance disaster relief efficacy.

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Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Models for Mining Social Media Data for Effective Natural Disaster Assessment

Mittal,

Karki,

Parasher

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous studies have used the CAS theory of complex relationships among multiple actors and their interdependent actions to study the complex network structure of interorganizational collaboration in disaster management (Kapucu 2009). For example, researchers in Chinese disaster management have used the CAS theory to investigate the network structure of interorganizational collaboration in disasters, including the Wenchuan and Lushan earthquakes (Comfort and Zhang 2020), the freezing rain and snow disasters in Xi'an (Guo and Kapucu 2015b), the 2016 Jiangsu-Funing tornado in Jiangsu Province (Zhang and Chen 2022), the Covid-19 pandemic (Guo et al 2021), the Lushan and Changning earthquakes, and the Tianjin Port and Xiangshui explosion accidents (Tao and Zhang 2023). Considering the complex flood disaster management process, we also adopted the CAS theory to explore the mechanism of interorganizational collaboration in flood disaster management from the network perspective.…”

Section: Complex Adaptive Systems (Cas) Perspective On Interorganizat...mentioning

confidence: 99%

The Dynamics of Interorganizational Collaboration in Disaster Management: A Network Study Based on Flood Disasters in China

Li,

Liu,

Liu

2023

Int J Disaster Risk Sci

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Interorganizational collaboration networks have become an important tool for disaster management. However, research on how different organizations can effectively collaborate throughout the entire disaster management process in centralized states such as China is scarce. This study begins to fill this lacuna by investigating interorganizational collaboration in different phases of disaster management and analyzing changes in the structure of the networks constructed during the preparedness and response phases of the 2020 flood disaster in Hubei Province, China. Building on the complex adaptive systems (CAS) theory, we argue that interorganizational collaboration changes dynamically according to its tasks and requirements. In the preparedness phase, interorganizational collaborations primarily follow established plans and choose horizontal self-organized collaboration mechanisms. However, when the urgent information and resource requirements increase in the response phase, many organizations choose vertical mandatory collaboration mechanisms. We found that organizations at the central and provincial levels in China were well positioned to coordinate information and resources and strengthen the interorganizational collaboration and communication that is crucial in disaster management. These findings contribute to the study of interorganizational collaboration networks in disaster management.

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Data mining of social media for urban resilience study: A case of rainstorm in Xi'an

Zhou

et al. 2023

International Journal of Disaster Risk Reduction

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Social Media Support Mechanisms for Organizational Adaptation in Governmental Response to Extreme Natural Hazards

Cited by 7 publications

References 36 publications

Machine Learning Models for Mining Social Media Data for Effective Natural Disaster Assessment

Machine Learning Models for Mining Social Media Data for Effective Natural Disaster Assessment

The Dynamics of Interorganizational Collaboration in Disaster Management: A Network Study Based on Flood Disasters in China

Data mining of social media for urban resilience study: A case of rainstorm in Xi'an

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