Using social media big data for tourist demand forecasting: A new machine learning analytical approach

Li, Yulei; Lin, Zhibin; Xiao, Sarah

Abstract

This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing.

Citation

Li, Y., Lin, Z., & Xiao, S. (2022). Using social media big data for tourist demand forecasting: A new machine learning analytical approach. Journal of digital economy, 1(1), 32-43. https://doi.org/10.1016/j.jdec.2022.08.006

• Journal Article Type: Article
• Acceptance Date: Aug 16, 2022
• Online Publication Date: Aug 27, 2022
• Publication Date: 2022-06
• Deposit Date: Aug 25, 2022
• Publicly Available Date: Sep 28, 2022
• Journal: Journal of Digital Economy
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Issue: 1
• Pages: 32-43
• DOI: https://doi.org/10.1016/j.jdec.2022.08.006
• Public URL: https://durham-repository.worktribe.com/output/1193202