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A Model to Measure Tourist Preference toward Scenic Spots Based on Social Media Data: A Case of Dapeng in China

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  • Yao Sun

    (School of Architecture and Urban Planning, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China
    Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China)

  • Hang Ma

    (School of Architecture and Urban Planning, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China)

  • Edwin H. W. Chan

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

Abstract

Research on tourist preference toward different tourism destinations has been a hot topic for decades in the field of tourism development. Tourist preference is mostly measured with small group opinion-based methods through introducing indicator systems in previous studies. In the digital age, e-tourism makes it possible to collect huge volumes of social data produced by tourists from the internet, to establish a new way of measuring tourist preference toward a close group of tourism destinations. This paper introduces a new model using social media data to quantitatively measure the market trend of a group of scenic spots from the angle of tourists’ demand, using three attributes: tourist sentiment orientation, present tourist market shares, and potential tourist awareness. Through data mining, cleaning, and analyzing with the framework of Machine Learning, the relative tourist preference toward 34 scenic spots closely located in the Dapeng Peninsula is calculated. The results not only provide a reliable “A-rating” system to gauge the popularity of different scenic spots, but also contribute an innovative measuring model to support scenic spots planning and policy making in the regional context.

Suggested Citation

  • Yao Sun & Hang Ma & Edwin H. W. Chan, 2017. "A Model to Measure Tourist Preference toward Scenic Spots Based on Social Media Data: A Case of Dapeng in China," Sustainability, MDPI, vol. 10(1), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:10:y:2017:i:1:p:43-:d:124420
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    References listed on IDEAS

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    1. Wang, Xia & Li, Xiang (Robert) & Zhen, Feng & Zhang, JinHe, 2016. "How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach," Tourism Management, Elsevier, vol. 54(C), pages 309-320.
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    Cited by:

    1. Feng Hu & Yijie Jin & Yaqian Niu & Keyi Chen, 2024. "Does the Tourist Appreciate Your Employees? Prioritizing Service Employee Improvements Based on User-Generated Content Mining," SAGE Open, , vol. 14(2), pages 21582440241, May.
    2. Honghong Wei & Mingming Cheng & Li Huang, 2024. "5A accreditation of tourist attractions in China and international tourist arrivals: A system generalised method of moments approach," Australian Economic Papers, Wiley Blackwell, vol. 63(4), pages 627-645, December.
    3. Weiwei Zhang & Lingling Jiang, 2021. "Effects of High-Speed Rail on Sustainable Development of Urban Tourism: Evidence from Discrete Choice Model of Chinese Tourists’ Preference for City Destinations," Sustainability, MDPI, vol. 13(19), pages 1-19, September.
    4. Alecxandrina Deaconu & Elena Mădălina Dedu & Ramona Ștefania Igreț & Cătălina Radu, 2018. "The Use of Information and Communications Technology in Vocational Education and Training—Premise of Sustainability," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    5. Chenghao Yang & Tongtong Liu, 2022. "Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review," Land, MDPI, vol. 11(10), pages 1-22, October.

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