Using big data to measure cultural tourism in Europe

International tourism statistics are notorious for being overly aggregated, lacking in detailed tourist information, not timely, and often provided only on an annual basis. We suggest a unique, complementary data-driven approach relying on big data collected from Tripadvisor. We obtain a systematic, consistent, and reliable approximation for tourism flows, with high precision, frequency, and depth of information. The approach provides also a list of all tourist attractions in a country. We validate the approach pursued and present one application of the data by illuminating the patterns and changes in travel flows in selected European destinations during and after the COVID-19 pandemic. This project opens a range of new research questions and possibilities for tourism economics and cultural economics.