A social media-based framework for tourist behaviour analysis and characterization in urban environments

Abstract. Tourism is a very important and fast growing industry worldwide that has generated 25% of all global net new jobs during the last 5 years. New tools can be valuable for relaunching the sector and provide alternative analysis and segmentation capabilities to organizations involved. We present an analysis and visualization framework for tourist behaviour study and segmentation based on tested methods and technologies, combined and extended in an innovative way. Our framework uses Flickr data as input and classifies users according to country of origin. Then, urban distribution patterns are obtained in two different spatial levels by using [Network] Kernel Density Estimation in 1D and 2D spaces, as well as spatial clustering with HDBSCAN. Basic Natural Language Processing is applied to extract and visualize semantics generated in the social media platform and a visualization of typologies of Points of Interest by nationality is proposed for the development of tourism dashboards. We have applied our framework to three European cities of different size to test the segmentation capabilities of the approach. Results suggest a good potential for tourism management in urban environments.