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A social media analytics perspective for human‐oriented smart city planning and management

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  • Shah Jahan Miah
  • Huy Quan Vu
  • Damminda Alahakoon

Abstract

Understanding how people engage in daily activities within a region can provide valuable information for smart city planners and strategic partners to use to assist in their decision‐making processes. Such insights may relate to economic activities, sustainable city design, environmental impacts, and responses to climate change, contributing to the improvement in the quality of human life. Considerable attention is recently directed towards smart city initiatives that benefit majority of people, rather than projects that cater to the political, architectural, or vanity needs of a minority. However, understanding citizen requirements, behaviors, and opinions is difficult, and requires the use of technology and appropriate information sources. While social‐media big data have provided opportunities to develop evidence‐based insights into human daily activities, effective analytical methods to harness these opportunities remain in development. We propose a new analytical method to provide a deeper understanding of citizen activities by constructing building blocks in their activity storylines, with analysis of these storylines providing evidence‐based insights into their activities. Results demonstrate the usefulness of our method to smart city planners and strategic partners, providing invaluable insights to assist them in making decisions regarding sustainable smart city development.

Suggested Citation

  • Shah Jahan Miah & Huy Quan Vu & Damminda Alahakoon, 2022. "A social media analytics perspective for human‐oriented smart city planning and management," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 119-135, January.
  • Handle: RePEc:bla:jinfst:v:73:y:2022:i:1:p:119-135
    DOI: 10.1002/asi.24550
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    1. Greene, Derek & Cross, James P., 2017. "Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach," Political Analysis, Cambridge University Press, vol. 25(1), pages 77-94, January.
    2. Sivarajah, Uthayasankar & Kamal, Muhammad Mustafa & Irani, Zahir & Weerakkody, Vishanth, 2017. "Critical analysis of Big Data challenges and analytical methods," Journal of Business Research, Elsevier, vol. 70(C), pages 263-286.
    3. Mike Thelwall & Olga Goriunova & Farida Vis & Simon Faulkner & Anne Burns & Jim Aulich & Amalia Mas-Bleda & Emma Stuart & Francesco D'Orazio, 2016. "Chatting through pictures? A classification of images tweeted in one week in the UK and USA," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(11), pages 2575-2586, November.
    4. Wilkinson, Leland & Friendly, Michael, 2009. "The History of the Cluster Heat Map," The American Statistician, American Statistical Association, vol. 63(2), pages 179-184.
    5. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.
    6. Rizwan Muhammad & Yaolong Zhao & Fan Liu, 2019. "Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China," Sustainability, MDPI, vol. 11(10), pages 1-30, May.
    7. Xiangbin Yan & Jing Wang & Michael Chau, 2015. "Customer revisit intention to restaurants: Evidence from online reviews," Information Systems Frontiers, Springer, vol. 17(3), pages 645-657, June.
    8. Higinio Mora & Raquel Pérez-delHoyo & José F. Paredes-Pérez & Rafael A. Mollá-Sirvent, 2018. "Analysis of Social Networking Service Data for Smart Urban Planning," Sustainability, MDPI, vol. 10(12), pages 1-19, December.
    9. Chua, Alvin & Servillo, Loris & Marcheggiani, Ernesto & Moere, Andrew Vande, 2016. "Mapping Cilento: Using geotagged social media data to characterize tourist flows in southern Italy," Tourism Management, Elsevier, vol. 57(C), pages 295-310.
    10. Camboim, Guilherme Freitas & Zawislak, Paulo Antônio & Pufal, Nathália Amarante, 2019. "Driving elements to make cities smarter: Evidences from European projects," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 154-167.
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    1. Irina A. Morozova & Stanislav S. Yatsechko, 2022. "The Risks of Smart Cities and the Perspectives of Their Management Based on Corporate Social Responsibility in the Interests of Sustainable Development," Risks, MDPI, vol. 10(2), pages 1-15, February.

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