Despite the growing importance of mobile tracking technology in urban planning and traffic forecasting, its utilization in the understanding of the basic laws governing tourist flows remains limited. Knowledge regarding the motivations and spatial behavior of tourists has great potential in sustainable tourism studies. In this paper, we combine social media (Twitter) and mobile positioning data (MPD) in the analysis of international tourism flows in Szeged, a secondary urban center in Hungary. First, the content of geotagged and non-geotagged Twitter messages referring to Szeged in a six-month period of 2018 was analyzed. In this way specific events attracting foreign tourists were identified. Then, using MPD data of foreign SIM cards, visitor peaks in the investigated period were defined. With the joint application of the social media and mobile positioning analytical tools, we were able to identify those attractions (festivals, sport and cultural events, etc.) that generated significant tourism arrivals in the city. Furthermore, using the mixed-method approach we were also able to analyze the movements of foreign visitors during one large-scale tourism event and evaluate its hinterland. Overall, this study supports the idea that social media data should be combined with other real-time data sources, such as MPD, in order to gain a more precise understanding of the behavior of tourists. The proposed analytical tool can contribute to methodological and conceptual development in the field, and information gained by its application can positively influence not only tourism management and planning but also tourism marketing and placemaking.

Even though tourism (both domestic and international) is one of the main triggering factors of human mobility worldwide, some of its forms are unexplored. This can be partly linked with the lack of reliable data and obstacles related to data processing and interpretation. Mobile Positioning Data (MPD) allows us to identify various forms of tourism that are undetectable through traditional data sources such as accommodation statistics. Using MPD, not only same-day tourists but also the real time mobility patterns of tourists among various destinations can be revealed, and even hidden (i.e., unobserved) forms of tourism can be detected. However, despite the obvious benefits of such data, very few comprehensive studies exist to date on the processing, and interpretation of MPD in tourism research. In this paper, a case study is presented on the challenges and opportunities of processing MPD from raw to good quality researchable data offering a baseline tool for MPD-based research in the field of tourism. With the methodology introduced in this paper, it is possible to provide a more accurate picture of tourist flows regarding unobserved tourists, including same-day visitors.