Mendieta Aragón, AdriánNavío Marco, JulioGarín Muñoz, María Teresa2024-08-292024-08-292024-04-25Mendieta-Aragón, A., Navío-Marco, J. and Garín-Muñoz, T. (2024), ""Twitter's capacity to forecast tourism demand: the case of way of Saint James"", European Journal of Management and Business Economics. https://doi.org/10.1108/EJMBE-09-2023-02952444-8494https://doi.org/10.1108/EJMBE-09-2023-0295https://hdl.handle.net/20.500.14468/23583The registered version of this article, first published in European Journal of Management and Business Economics, is available online at the publisher's website: Emerald Publishing, https://doi.org/10.1108/EJMBE-09-2023-0295La versión registrada de este artículo, publicado por primera vez en European Journal of Management and Business Economics, está disponible en línea en el sitio web del editor: Emerald Publishing, https://doi.org/10.1108/EJMBE-09-2023-0295Purpose – Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination. Design/methodology/approach – This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables. Findings – The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism. Originality/value – This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.eninfo:eu-repo/semantics/openAccess53 Ciencias Económicasartículohotel demand forecastingsocial mediabig datatwitterpilgrimage tourism