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.
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