67 USER-GENERATED CONTENT IN TOURISM – Contributions by Eva Martin-Fuentes
67 USER-GENERATED CONTENT IN TOURISM – Contributions by Eva Martin-Fuentes
Introduction
My initiation to the world of research and teaching in tourism and the hospitality industry came rather late since before devoting myself to it, for almost 20 years, my career focused on the management of cultural activities and congress and conference organization. That is in addition to overseeing tourism promotion for the Lleida Provincial Council Tourist Board in Catalonia, Spain.
When I worked in the Tourist Board of my home region, the opportunity arose to dedicate myself to university teaching at the University of Lleida since the bachelor’s degree in Tourism was just starting, and I began to work as a part-time lecturer to provide a practical standpoint of event management and promoting tourism.
I slowly began to develop a passion for research in tourism (I had already been passionate about tourism for many years) and the hospitality industry, and I embarked on research using user-generated content (UGC) in tourism and in the hospitality industry.
This chapter presents the various contributions made over the last five years in research in tourism, especially in the analysis of UGC.
Literature Review
During my early years, in collaboration with researchers from my department at the University of Lleida, our research focused on issues close to our region and even our institution. We performed research into the implementation of e-commerce in ski resorts in Spain and Andorra (Cristobal-Fransi, Daries-Ramon, Mariné-Roig, & Martin-Fuentes, 2017).
I then began to work in conjunction with lecturers of the Department of Computer Engineering at my University on issues related to the hospitality industry based on user-generated content on such platforms as TripAdvisor or booking platforms that allow feedback from users, such as Booking.com.
We verified whether ratings given by guests in hotels all over the world match hotel categories, confirming that there is a relationship between guests’ opinions and the hotel classification system (Martin-Fuentes, 2016), and that possible false opinions due to the anonymity of opinions on platforms such as TripAdvisor do not alter the position in the rankings of hotels worldwide compared with the opinions on platforms where the user is authenticated, such as Booking.com, with data from hotels of more than 400 tourist destinations around the world (Martin-Fuentes, Mateu, & Fernandez, 2018).
We confirmed that hotel categories worldwide can be inferred from the features appraised by users, and we created a model with machine learning techniques to classify any type of accommodation, for example, properties offered by Airbnb. This work has so far been the most cited (Martin-Fuentes, Fernandez, Mateu, & Marine-Roig, 2018).
We have also worked with researchers from other Spanish Universities in studies that analyse which hotels are most dependent on the Online Travel Agency (OTA), Booking.com, from the number of reviews posted by