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.
Original language | English |
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Article number | 1295 |
Journal | Sustainability |
Volume | 15 |
Issue number | 2 |
DOIs | |
State | Published - Jan 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 by the authors.
Keywords
- machine learning
- natural language processing
- social media
- studentification
- sustainability
- text mining
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Geography, Planning and Development
- Renewable Energy, Sustainability and the Environment
- Environmental Science (miscellaneous)
- Energy Engineering and Power Technology
- Hardware and Architecture
- Computer Networks and Communications
- Management, Monitoring, Policy and Law