# Using social network and semantic analysis to analyze online travel forums and forecast tourism demand

arXiv: https://arxiv.org/abs/2105.07727 · HTML: https://mlsanity.com/paper/2105.07727/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 97 references that are in the map.
- First posted on arXiv 17 May 2021.

## Built on by (most cited first, 4 of 4)

- [Location reference recognition from texts: A survey and comparison](https://mlsanity.com/paper/2207.01683/) (2022)
- [Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)](https://mlsanity.com/paper/2109.12630/) (2021)
- [Geo-Semantic-Parsing: AI-powered geoparsing by traversing semantic knowledge graphs](https://mlsanity.com/paper/2503.01386/) (2025)
- [Tourist arrival forecasting with game-theoretic social influence weighting and deep learning](https://mlsanity.com/paper/2507.03411/) (2025)

## Abstract (arXiv)

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.