Toronto's Eiffel Tower: Assessing tourist attraction similarities through user-contributed content

Authors: Grant McKenzie*, McGill University
Topics: Geographic Information Science and Systems, Tourism Geography, Spatial Analysis & Modeling
Keywords: tourism, giscience, place, data driven, user-generated content, similarity
Session Type: Paper
Day: 4/6/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Congressional A, Omni, West
Presentation File: No File Uploaded

Tourists are often driven to visit a city by the uniqueness of the attractions available within the region. The interest in these locations varies by visitor as some tourists are interested in a single specific attraction while others prefer thematic travel. Travelers today have access to the shared experiences of other visitors in the form of user-contributed text reviews, opinions, photographs, and videos, all contributed through online travel sites. The data accessible through these platforms offer a novel opportunity to examine the similarities and difference between tourist attractions, their cities, and the travelers that contribute reviews. In this work I introduce a couple of data-driven approaches to assessing similarity through spatial statistics, and the textual analysis of user-contributed reviews. This approach identifies nuanced differences and similarities in the ways that reviewers write about cities and the attractions contained within. Furthermore, this work uncovers an interesting pattern of reviewers finding similarities between attractions they are visiting and attractions in their home country.

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