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Tourism demand forecasting with online news data mining

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  • Park, Eunhye
  • Park, Jinah
  • Hu, Mingming

Abstract

This study empirically tests the role of news discourse in forecasting tourist arrivals by examining Hong Kong. It employs structural topic modeling to identify key topics and their meanings related to tourism demand. The impact of the extracted news topics on tourist arrivals is then examined to forecast tourism demand using the seasonal autoregressive integrated moving average with the selected news topic variables method. This study confirms that including news data significantly improves forecasting performance. Our forecasting model using news topics also outperformed the others when the destination was experiencing social unrest at the local level. These findings contribute to tourism demand forecasting research by incorporating discourse analysis and can help tourism destinations address various externalities related to news media.

Suggested Citation

  • Park, Eunhye & Park, Jinah & Hu, Mingming, 2021. "Tourism demand forecasting with online news data mining," Annals of Tourism Research, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:anture:v:90:y:2021:i:c:s0160738321001511
    DOI: 10.1016/j.annals.2021.103273
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    Cited by:

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    2. Piselli, C. & Fronzetti Colladon, A. & Segneri, L. & Pisello, A.L., 2022. "Evaluating and improving social awareness of energy communities through semantic network analysis of online news," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. Mohamed M. Mostafa, 2023. "A one-hundred-year structural topic modeling analysis of the knowledge structure of international management research," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3905-3935, August.
    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.
    5. Ruochen Yang & Kun Liu & Chang Su & Shiro Takeda & Junhua Zhang & Shuhao Liu, 2023. "Quantitative Analysis of Seasonality and the Impact of COVID-19 on Tourists’ Use of Urban Green Space in Okinawa: An ARIMA Modeling Approach Using Web Review Data," Land, MDPI, vol. 12(5), pages 1-25, May.

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