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Novel Use of Social Media Big Data and Artificial Intelligence for Community Resilience Assessment (CRA) in University Towns

Author

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  • Mohammed Abdul-Rahman

    (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China
    Department of Urban and Regional Planning, University of Lagos, Lagos 101017, Nigeria
    AI Africa Lab, Lagos 101017, Nigeria)

  • Mayowa I. Adegoriola

    (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)

  • Wilson Kodwo McWilson

    (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)

  • Oluwole Soyinka

    (School of Public Policy and Global Affairs, University of British Columbia, Vancouver Campus, Vancouver, BC V6T 1Z2, Canada)

  • Yusuf A. Adenle

    (Department of Geography and Resource Management, Chinese University of Hong Kong, Hong Kong, China)

Abstract

University towns face many challenges in the 21st century due to urbanization, increased student population, and higher educational institutions’ inability to house all their students on-campus. For university towns to be resilient and sustainable, the challenges facing them must be assessed and addressed. To carry out community resilience assessments, this study adopted a novel methodological framework to harness the power of artificial intelligence and social media big data (user-generated content on Twitter) to carry out remote studies in six university towns on six continents using Text Mining, Machine Learning, and Natural Language Processing. Cultural, social, physical, economic, and institutional and governance community challenges were identified and analyzed from the historical big data and validated using an online expert survey. This study gives a global overview of the challenges university towns experience due to studentification and shows that artificial intelligence can provide an easy, cheap, and more accurate way of conducting community resilience assessments in urban communities. The study also contributes to knowledge of research in the new normal by proving that longitudinal studies can be completed remotely.

Suggested Citation

  • Mohammed Abdul-Rahman & Mayowa I. Adegoriola & Wilson Kodwo McWilson & Oluwole Soyinka & Yusuf A. Adenle, 2023. "Novel Use of Social Media Big Data and Artificial Intelligence for Community Resilience Assessment (CRA) in University Towns," Sustainability, MDPI, vol. 15(2), pages 1-30, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1295-:d:1031396
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    References listed on IDEAS

    as
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    Cited by:

    1. Donghyun Kim & Sanghoon Lee & Seiyong Kim, 2023. "Study of Campustown Projects for the Sustainable Win-Win Growth of Universities and Communities," Sustainability, MDPI, vol. 15(13), pages 1-26, June.
    2. Mohammed Abdul-Rahman & Wale Alade & Shahnawaz Anwer, 2023. "A Composite Resilience Index (CRI) for Developing Resilience and Sustainability in University Towns," Sustainability, MDPI, vol. 15(4), pages 1-27, February.

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