  Forecasting tourism demand has important implications for both policy makers
and companies operating in the tourism industry. In this research, we applied
methods and tools of social network and semantic analysis to study
user-generated content retrieved from online communities which interacted on
the TripAdvisor travel forum. We analyzed the forums of 7 major European
capital cities, over a period of 10 years, collecting more than 2,660,000
posts, written by about 147,000 users. We present a new methodology of analysis
of tourism-related big data and a set of variables which could be integrated
into traditional forecasting models. We implemented Factor Augmented
Autoregressive and Bridge models with social network and semantic variables
which often led to a better forecasting performance than univariate models and
models based on Google Trend data. Forum language complexity and the
centralization of the communication network, i.e. the presence of eminent
contributors, were the variables that contributed more to the forecasting of
international airport arrivals.
