Using social network and semantic analysis to analyze online travel forums and forecast tourism demand
This work addresses forecasting challenges for policy makers and tourism companies, offering an incremental improvement by incorporating new data sources into existing models.
The researchers tackled tourism demand forecasting by analyzing 2.66 million posts from TripAdvisor forums over 10 years, finding that integrating social network and semantic variables into models improved forecasting performance compared to univariate and Google Trend-based models.
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.