Uncovering Tourist Visit Intentions on Social Media through Sentence Transformers

Abstract

The problem of understanding and predicting tourist behavior in choosing their destinations is a long-standing one. The first step in the process is to understand users’ intention to visit a country, which may later translate into an actual visit. Would-be tourists may express their intention to visit a destination on social media. Being able to predict their intention may be useful for targeted promotion campaigns. In this paper, we propose an algorithm to predict visit (or revisit) intentions based on the texts in posts on social media. The algorithm relies on a neural network sentence-transformer architecture using optimized embedding and a logistic classifier. Employing two real labeled datasets from Twitter (now X) for training, the algorithm achieved 90% accuracy and balanced performances over the two classes (visit intention vs. no-visit intention). The algorithm was capable of predicting intentions to visit with high accuracy, even when fed with very imbalanced datasets, where the posts showing the intention to visit were an extremely small minority.