## 5 Discussion, Conclusions, and Implications  
The main contribution of the study is the introduction of a method for analyzing social media data that create visitor profiles according to their travel preferences and mobility. This study corroborates that social media are very useful platforms as sources for big data research in the field of tourism [8, 30], and they can also be used to study tourist mobility [26, 35, 41, 55]. It also improves the knowledge about the different mobility patterns of tourists depending on who they are and what their preferences are [56]. Therefore, the study has shown that clustering visitors by their travel mobility permits uncovering much more information about visitors and their preferences than previous studies. It can also provide complementary information to DMOs, attraction operators, and developers of contextual and next-POI recommender systems [81]. Additionally, the study also reveals the most popular or the most visited points of interest at destinations. Previous studies had also found out the most popular tourist spots or the most visited routes by analyzing geo-tagged social media data [62, 66], but their analysis did not allow for knowing which were the most visited POIs by the different tourists. Thus, interestingly, the applied method allows for knowing the percentages of tourists in each cluster who visit each attraction the most. This is crucial for the managers of the different tourist attractions that want to know who their majority visitors are as well as their interests; in that way, they will be able to adapt their service and information in an almost personalized way. Another contribution of the study is to show the mobility of each cluster of tourists between POIs and to see graphically the movement that they make in the map of the destination. Previous studies have shown mobility with place maps and most visited points [26]. Many studies on GSMD only focused on tourists' mobility patterns [48, 59, 73]. However, the difference in mobility patterns between sub-groups or clusters of tourists has not been fully researched [37]. Therefore, GSMD analysis allows for knowing the dispersion and tourists' movements, the routes they follow, the activities they carry out in a territory [57, 58, 59, 60, 61, 62], and their density of movements [63] and flows [38, 64, 65]. Hence, the resulting information is particularly valuable for city management, since it provides a  
Figure 5: Tourist mobility pattern between attractions in Barcelona for cluster 16.  
better knowledge of the connections between points of interest related to different clusters of tourists according to their preferences and behaviors. This information can help DMOs to define new tourist routes, to create targeted marketing campaigns, to optimize transport routes between heavily connected POIs for different types of tourists, or to improve the management of congestion or overcrowding situations. To sum up, through this application of Artificial Intelligence techniques to social media data creating clusters of tourists, it has been possible to know how to segment them according to their visitor behavior and visit preferences. This information is key to the DMOs and the different service providers of the destinations. The interest of DMOs in analyzing big data and knowing the maximum information about their tourists is based on being able to anticipate their interests and preferences [38]. This is precisely the information that this study provides. Therefore, the study can have a major impact on the marketing and flows management of tourist destinations. The exploration of the relationship between tourists' profiles, points of interest, and tourist mobility allows for gaining further insight into really concerning debates on tourism pressure in specific locations, and destination carrying capacity.