## 2 Theoretical Framework  
### Tourist Flow Analysis and Social Media Analytics  
Prior to the technological developments, studies on tourist mobility were based on tourists' surveys [35], and they were rather limited. Tourist flow analysis grew enormously with the development of tracking mechanisms like GPS, cell-tower identification or Wi-Fi positioning [41], which have made it possible to obtain big data from the movement of tourists at destinations. Several studies have used GPS [27; 42; 43] to find out which were the most visited places in a particular destination and when they were visited. In destinations, the proliferation of sensor networks and portable devices like smartphones has also made it possible to obtain big data from tourists and to know their movements or flows [26]. In general, the increasing effectiveness and reliability of GPS data and the mobile positioning data have increased the possibilities of analyzing spatial-temporal behaviors, widening the research objectives beyond the initial aim to know where and when visitors went. In this vein, they have been used to identify seasonal demand patterns by [PERSON] et al. [44] or to improve the management of destination marketing by [PERSON] et al. [45]. Other authorshave considered data obtained from mobility services, such as the subway smart card [46] or bike sharing systems [47]. Social media also has very useful platforms for knowing the movements of tourists. Social media analytics (SMA) is a research field that has advanced heavily since 2014, but it is still in an early stage of development [32]. The potential of social media as sources for big data research in the field of tourism has increased in the last decade [8,30], and studies on big data, social media, UGC (User-Generated Content), and online reviews have proliferated in hospitality and tourism [30,32,34,48,49]. Moreover, the innumerable footprints that millions of tourists leave online using the technological platforms constitute an interesting source for knowing the tourists' movements and flows [49,50], although big data-based theoretical studies still remain limited [34]. Text analytics and data mining studies try to find out tourists' interests and to predict their decisions and behaviors [51]. Trend analysis studies also try to predict tourists' behaviors [52] or future trends in tourist behavior at destinations [53]. Nevertheless, one of the best ways to know the behavior of tourists during the trip is through spatial data analysis. Spatial data analysis is a stream of studies within SMA that, through the analysis of GSMD [8], aim to ascertain the spatial distribution of tourists on a place or a destination [54] and even to know tourists' movements or flows [26,41,55]. Flow is the collective movement of people [26], and flow analysis shows the movement of tourists in a location [38]. GSMD are key sources of information to analyze tourists flows [56] in order to uncover their travel preferences and behaviors [26] or the tourists' experiences through a spatial analysis [33]. [PERSON] et al. [50] compared GSMD results with the UNWTO record-based network demonstrating the usefulness of GSMD to discover tourist flows. However, there are still few publications that analyze Geotagged Big Data to examine the spatial distribution and flows of different types of tourists in the destinations [35]. GSMD analysis allows for knowing the dispersion of tourists and also the routes and activities they carry out in the destination [57, 58, 59, 60, 61, 62], their density of movements [63], their flows [26, 38,64,65], and the most popular resorts, attractions, or points of interest in the destinations [66, 67, 36]. Most studies that analyze GSMD have focused on Twitter because it is a platform that has global coverage and its data are available for free on the Internet from the moment the tweets are published [35]. Twitter is, along with Tripadvisor, one of the two most used platforms by SMA.