2025/11/21 by Demessance, Theo, Bi, Chongke, Djebali, Sonia +1
Social Sciences · #Artificial Intelligence (cs.AI) #Digital Marketing and Social Media #Diverse Aspects of Tourism Research #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Machine Learning (cs.LG)
paper · doi:10.48550/arxiv.2511.19465
openalex publication_date 2025/11/21 · openalex created_date 2025/11/28 · openalex updated_date 2026/07/28
Nowadays, social networks are becoming a popular way of analyzing tourist behavior, thanks to the digital traces left by travelers during their stays on these networks. The massive amount of data generated; by the propensity of tourists to share comments and photos during their trip; makes it possible to model their journeys and analyze their behavior. Predicting the next movement of tourists plays a key role in tourism marketing to understand demand and improve decision support. In this paper, we propose a method to understand and to learn tourists' movements based on social network data analysis to predict future movements. The method relies on a machine learning grammatical inference algorithm. A major contribution in this paper is to adapt the grammatical inference algorithm to the context of big data. Our method produces a hidden Markov model representing the movements of a group of tourists. The hidden Markov model is flexible and editable with new data. The capital city of France, Paris is selected to demonstrate the efficiency of the proposed methodology.