Reading the social preferences of tourist destinations through social media data

Abstract

The social preferences of individuals have been traditionally identified through traditional means using field techniques such as direct interviewing, observation and people-counting. The virtual layer of the social system currently allows new ways to identify the most preferred urban areas or venues. With that in mind, this paper aims to study how data from two Location-Based Social Networks: Foursquare and Twitter can shed light on empirical and theoretical observations about the spatial patterns characterizing where people tend to be and socialise in a tourist city. The methodology proposed consists of three stages. First, a self-developed desktop application retrieves geospatial data from the selected social networks. Then, the dataset obtained is organised and sorted. Finally, the georeferenced data is visualised and analysed and the trends are noted and discussed. To that end, the city of Benidorm was selected as a case study and the data was collected during the off-peak tourist season. The results demonstrate a correlation between the empirical assumptions and the findings from the social networks analysis about people’s preferred places. Foursquare provides a ranking of urban spaces and venues related to tourism, and the location of the tweets confirms the seasonal nature of Benidorm. Despite the fact that information from location-based social media has to be treated carefully, since each service has its own unique purpose, the method proposed has proven to be effective and reliable to depict a representative sample of people’s social patterns and preferences in tourist cities.