## 4 Results  
### Characterization of the Visitors in Each Cluster  
The analysis identified different clusters with a wide range of visit preferences. Figure 1 highlights the main characteristics shared by tourists in each of the 20 clusters, including their origin, the kind of leisure activities they visit, their preferred time of the day, and their interest in different kinds of activities. All clusters are associated with one or more types of activities, which match the activity features used in the clustering process. As it can be seen in Figure 1, the historic and religious activities (columns A and B) are the ones associated with most clusters (0, 2, 5, 8, 11, 16, 17, and 19). In most of these clusters, users enjoy visiting the most popular POIs, revealing that the historic and religious attractions are among the most popular in the city of Barcelona. This also proves a direct correlation between tourist interests and their popularity, as it could be expected. It can also be seen that the individuals in clusters 2 and 17 visit a larger number of popular POIs and have a wider spread of different kinds of activities. These clusters contain mostly non-Spanish tourists, who visit and experience many varieties of POIs that the city offers. On the other hand, clusters characterized mainly by Spanish nationals (clusters 1, 3, 9, 10, 15, 21, 23, and 24) focus on a small set of features and on unpopular POIs, indicating a higher specificity in their favorite POIs and their visits. People in cluster 1, for example, prefer to do scenic routes within the city. However, those in cluster 3 prefer to go to the beach or places near the beach, and some historic sites. Tourists on cluster 9 visit Barcelona mainly for health and care, although they also visit religious sites and cafes, whereas those in cluster 15 prefer food and entourism, those in cluster 23 are mainly interested in visiting Camp Nou, the football stadium, and people in cluster 24 mainly visit museums. The mobility analysis based on visitor clusters that was carried out also allows for knowing the specific POIs visited by the tourists of each cluster. Figure 2 shows the percentage of tourists from each cluster who visit the top 20 most visited POIs of the destination, highlighting in each row the values above the average. For example, Camp Nou, the Barcelona football stadium, is visited mostly by tourists from cluster 23, the religious architecture (as the Sagrada Famdlia) and historic monuments (as the Palau de la Generalitat or the Casa Batllo) are mainly visited by tourists of clusters 0, 2, or 17, and beaches (like Barcelona) or places near them by tourists of cluster 3. This figure also clearly identifies those clusters that are focused on the most popular POIs, and to what degree. Clusters 0 and 2 are heavily focused on popular POIs as they are an above average representation of 55% and 80% (respectively) in the popular POIs. In contrast, clusters 7, 12, 13, 15, 21, and 23 have very little representation in the popular POIs. This shows that the 20 clusters do not favor only popular or unpopular POIs. Figure 1: Cluster characteristics highlighting tourists’ origin, preferred tour time, orientation towards popular POIs, and interests in different activities. * Note: A—Religious, B—Historic, C—Routes, D—Nature, E—Art Gallery, F—Recreational Facilities, G—Beach, H—Cultural Amenities, I—Shopping, J—Nightlife, K—Statues, L—Health & Care, M—Viewpoints, N—Sculptures, O—Accommodation, P—Food, Q—Entourism, R—Amusement Parks, S—Museums, T—Architecture, U—Other Artworks, V—Art Centers. Figure 2: Top 20 most visited attractions and the percentage of their tweets per cluster, highlighting values above average in green. In summary, the clusters present unique groups of tourists with different characteristics and preferences derived from the clustering features and allows knowing which are the visitors of the most popular tourist POIs.