Enhancing smartness in second-home tourism destinations through social sensing for predicting occupancy levels

Abstract

Tourism is one of the most relevant socio-economic sectors worldwide. However, intensive tourism has caused significant social, urban, and environmental problems. In order to improve tourism management processes and within the context of a smart tourism scenario, renewed management approaches are emerging with the aim to use the latest IT technologies to increase profits and offer new sustainable models in tourism destinations. Importantly, one key issue in tourism destinations for supporting management and planning is predicting tourist occupancy. Unfortunately, the so-called second-home tourism destinations have no reliable accommodation data coming from hospitality establishments. To overcome this pitfall, in this article, the prediction of tourist occupancy is presented based on the analysis of residential accommodation booking data and people’s comments on social networks. The analysis focuses on Torrevieja (South-eastern Spain); one of the most important second-home tourist destinations worldwide. On one hand, an ARIMA model is carried out with the time series of AirBnB bookings. On the other hand, Twitter data related to Torrevieja is analyzed by identifying main topics and entities. Our results show that AirBnB bookings estimation can be made by measuring the number of people sending posts on Twitter about tourism-related topics.