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Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories

Author

Listed:
  • Tan Yigitcanlar

    (School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

  • Massimo Regona

    (School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

  • Nayomi Kankanamge

    (Department of Town and Country Planning, University of Moratuwa, Bandaranayaka Mawatha, Katubedda, Moratuwa 10400, Sri Lanka)

  • Rashid Mehmood

    (High Performance Computing Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Justin D’Costa

    (School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

  • Samuel Lindsay

    (School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

  • Scott Nelson

    (School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

  • Adiam Brhane

    (School of Architecture and Built Environment, Queensland University of Technology, 2 George Street, Brisbane, QLD 4000, Australia)

Abstract

Natural hazard-related disasters are disruptive events with significant impact on people, communities, buildings, infrastructure, animals, agriculture, and environmental assets. The exponentially increasing anthropogenic activities on the planet have aggregated the climate change and consequently increased the frequency and severity of these natural hazard-related disasters, and consequential damages in cities. The digital technological advancements, such as monitoring systems based on fusion of sensors and machine learning, in early detection, warning and disaster response systems are being implemented as part of the disaster management practice in many countries and presented useful results. Along with these promising technologies, crowdsourced social media disaster big data analytics has also started to be utilized. This study aims to form an understanding of how social media analytics can be utilized to assist government authorities in estimating the damages linked to natural hazard-related disaster impacts on urban centers in the age of climate change. To this end, this study analyzes crowdsourced disaster big data from Twitter users in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method and conducts sentiment and content analyses of location-based Twitter messages ( n = 131,673) from Australia. The study informs authorities on an innovative way to analyze the geographic distribution, occurrence frequency of various disasters and their damages based on the geo-tweets analysis.

Suggested Citation

  • Tan Yigitcanlar & Massimo Regona & Nayomi Kankanamge & Rashid Mehmood & Justin D’Costa & Samuel Lindsay & Scott Nelson & Adiam Brhane, 2022. "Detecting Natural Hazard-Related Disaster Impacts with Social Media Analytics: The Case of Australian States and Territories," Sustainability, MDPI, vol. 14(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:810-:d:722633
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    Citations

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    Cited by:

    1. Istiak Ahmad & Fahad Alqurashi & Ehab Abozinadah & Rashid Mehmood, 2022. "Deep Journalism and DeepJournal V1.0: A Data-Driven Deep Learning Approach to Discover Parameters for Transportation," Sustainability, MDPI, vol. 14(9), pages 1-72, May.
    2. Nala Alahmari & Sarah Alswedani & Ahmed Alzahrani & Iyad Katib & Aiiad Albeshri & Rashid Mehmood, 2022. "Musawah: A Data-Driven AI Approach and Tool to Co-Create Healthcare Services with a Case Study on Cancer Disease in Saudi Arabia," Sustainability, MDPI, vol. 14(6), pages 1-41, March.
    3. Eman Alqahtani & Nourah Janbi & Sanaa Sharaf & Rashid Mehmood, 2022. "Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery Using BERT Modelling," Sustainability, MDPI, vol. 14(20), pages 1-65, October.
    4. Raniah Alsahafi & Ahmed Alzahrani & Rashid Mehmood, 2023. "Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations," Sustainability, MDPI, vol. 15(5), pages 1-64, February.

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