## 1 Introduction  
Technological evolution has brought changes in tourists and their behavior [1, 2, 3], and it has catalyzed new information challenges for destinations. In this evolution, some destinations have started to become smart by integrating technological infrastructures and end-user devices [4, 5, 6]. However, the fulfillment of two of the main objectives of smart destinations, namely the enhancement of tourists' experiences and the improvement of their management, is still far from complete, and results from the destinations' efforts remain largely unreported [7]. Progress in this area requires Destination Management Organizations (DMOs) to know who their tourists are, what needs and travel preferences they have, what they visit the most, and which are their mobility patterns and flows around the destination [8]. Technological evolution has allowed some Destination Management Organizations to start to maintain personalized and real-time exchange of information with tourists [9] and, at the same time, to collect vast amounts of information from them (big data). The analysis of these data permits offering them even more personalized services [10, 11] at the moment and place they need it [12, 13]. This two-way communication between tourists and DMOs helps to generate more satisfactory tourist experiences [5; 14; 15] and to improve the destination's management [16]. Many studies have focused on knowing who are the visitors of a destination [17]. These studies have classified tourists in clusters or profiles depending on their travel preferences and behaviors [18; 19]. Some studies have analyzed the acceptance of technology by tourists [20], while others have focused on their degree of connectivity during the trip [21; 22]. To find out their preferences and behaviors, some authors have analyzed their information searches during the trip [23; 24; 25] and others their movements at the destination (flow analysis) through GPS or other smart technologies [26; 27]. Knowing the mobility patterns of tourists is one of the most important issues for the development of tourism planning and destination management [28; 29]. Social media has brought a great transformation in the related tourism research [8; 30; 31]. Social media analytics use natural language processing and machine learning techniques to analyze social media content [32]. Geotagged Social Media Data (GSMD) offer information about tourist behavior, travel route choices, emotions, and satisfaction level [33]. Nowadays, technology and analytical tools are evolving and social media analytics allow us to know the movements or flows of tourists [34]. Twitter is a platform with global coverage, very useful for tourist mobility analysis [35] that even allows us to determine the most visited POIs (Points of Interest) at destinations [36]. Existing research in spatial data or mobility/flow analysis through GSMD still offers little information about mobility patterns of different sub-groups or clusters of tourists [37]. Nevertheless, the more DMOs know about the mobility, preferences and behavior of tourists, the more they will be able to offer personalized services, packages and information [11; 38]. Additionally, the more they can automatize processes to obtain information from open social media data, the more efficient they will be when providing personalized information throughout travel recommender systems [39]. Therefore, the aim of the study is to develop a methodology that, through the application of Artificial Intelligence techniques to geotagged social media data, creates clusters of tourists according to their mobility patterns and visiting preferences at the destination. The analytical and managerial goal is to know the most visited points of interest and the main flows between them within a destination for each tourist cluster. This will allow the DMOs to uncover their tourists' profiles, their preferences, and their mobility patterns at the destination.