A Machine Learning Based Method for Automatic Identification of Disaster Related Information Using Twitter Data

Christidou, Athina Ntiana; Drakaki, Maria; Linardos, Vasileios

doi:10.1007/978-3-031-09176-6_8

Lecture Notes in Networks and Systems

2022

DOI: 10.1007/978-3-031-09176-6_8

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A Machine Learning Based Method for Automatic Identification of Disaster Related Information Using Twitter Data

Athina Ntiana Christidou,

Maria Drakaki,

Vasileios Linardos

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2024

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Cited by 2 publications

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Environmental Impact Assessment for Spatial Data Analysis in Disaster Management Using Machine Learning Multi-Criteria Resources

Ashifa,

Babu,

Safaei

et al. 2024

Remote Sens Earth Syst Sci

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Environmental Impact Assessment for Spatial Data Analysis in Disaster Management Using Machine Learning Multi-Criteria Resources

Ashifa,

Babu,

Safaei

et al. 2024

Remote Sens Earth Syst Sci

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Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response Management

Calle Müller,

Lagos,

Elzomor

2024

Sustainability

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Natural disasters cause extensive infrastructure and significant economic losses, hindering sustainable development and impeding social and economic progress. More importantly, they jeopardize community well-being by causing injuries, damaging human health, and resulting in loss of life. Furthermore, communities often experience delayed disaster response. Aggravating the situation, the frequency and impact of disasters have been continuously increasing. Therefore, fast and effective disaster response management is paramount. To achieve this, disaster managers must proactively safeguard communities by developing quick and effective disaster management strategies. Disruptive technologies such as artificial intelligence (AI), machine learning (ML), and robotics and their applications in geospatial analysis, social media, and smartphone applications can significantly contribute to expediting disaster response, improving efficiency, and enhancing safety. However, despite their significant potential, limited research has examined how these technologies can be utilized for disaster response in low-income communities. The goal of this research is to explore which technologies can be effectively leveraged to improve disaster response, with a focus on low-income communities. To this end, this research conducted a comprehensive review of existing literature on disruptive technologies, using Covidence to simplify the systematic review process and NVivo 14 to synthesize findings.

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A Machine Learning Based Method for Automatic Identification of Disaster Related Information Using Twitter Data

Cited by 2 publications

References 12 publications

Environmental Impact Assessment for Spatial Data Analysis in Disaster Management Using Machine Learning Multi-Criteria Resources

Environmental Impact Assessment for Spatial Data Analysis in Disaster Management Using Machine Learning Multi-Criteria Resources

Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response Management

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