Touristic site attractiveness seen through Twitter

TL;DR

This paper uses geolocated Twitter data to evaluate and rank the attractiveness of 20 major tourist sites worldwide, analyze visitor origins, and explore inter-site relationships.

Contribution

It introduces a novel method leveraging Twitter data to assess tourist site attractiveness, visitor origin distribution, and inter-site connectivity.

Findings

Top-ranked sites include the Taj Mahal, Pisa Tower, and Eiffel Tower.

Visitors predominantly come from their country of residence.

Network analysis reveals interconnectedness among tourist sites.

Abstract

Tourism is becoming a significant contributor to medium and long range travels in an increasingly globalized world. Leisure traveling has an important impact on the local and global economy as well as on the environment. The study of touristic trips is thus raising a considerable interest. In this work, we apply a method to assess the attractiveness of 20 of the most popular touristic sites worldwide using geolocated tweets as a proxy for human mobility. We first rank the touristic sites based on the spatial distribution of the visitors' place of residence. The Taj Mahal, the Pisa Tower and the Eiffel Tower appear consistently in the top 5 in these rankings. We then pass to a coarser scale and classify the travelers by country of residence. Touristic site's visiting figures are then studied by country of residence showing that the Eiffel Tower, Times Square and the London Tower welcome the majority of the visitors of each country. Finally, we build a network linking sites whenever a user has been detected in more than one site. This allow us to unveil relations between touristic sites and find which ones are more tightly interconnected.