Tourism-related placeness feature extraction from social media data using machine learning models

Abstract

The study of placeness has been focus for researchers trying to understand the impact of locations on their surroundings and tourism, the loss of it by globalization and modernization and its effect on tourism, or the characterization of the activities that take place in them. Identifying places that have a high level of placeness can become very valuable when studying social trends and mobility in relation to the space in which the study takes place. Moreover, places can be enriched with dimensions such as the demographics of the individuals visiting such places and the activities the carry in them thanks to social media and modern machine learning and data mining methods. Such information can prove to be useful in fields such as urban planning or tourism as a base for analysis and decision-making or the discovery of new social hotspots or sites rich in cultural heritage. This manuscript will focus on the methodology to obtain such information, for which data from Instagram is used to feed a set of classification models that will mine demographics from the users based on graphic and textual data from their profiles, gain insight on what they were doing in each of their posts and try to classify that information into any of the categories discovered in this article. The goal of this methodology is to obtain, from social media data, characteristics of visitors to locations as a discovery tool for the tourism industry.