Measuring destination image: a novel approach based on visual data mining.

Abstract

Availability of User Generated Content and the development of Big Data and machine learning algorithms have paved the way to collecting and analysing great volumes of data. We scan imagery data from traveling-related posts on Instagram to identify the key features of the destination image and of its dynamics. Specifically, we exploit a newly introduced Visual Object Recognition tool (Google Cloud Vision) to convert into textual labels the content of about 860,000 travel-related pictures posted on Instagram in Summer 2019 for several European islands. The output, a vector of labels’ frequencies on a very fine-grained scale, is used to proxy the destination image at different points in time. We then introduce the Index of Distance in Destination Image, a metric built on the pictures’ labels ranking, and aimed at providing a quantitative measure of (dis)similarity between destination images. We show that the analysis of labels and the index are fit to compare destinations cross-sectionally and over time, providing a useful tool for researchers, marketers and DMOs. We also deliver evidence on how external shocks (like extreme events linked to climate change) or the organization of events modify the cognitive sphere of the destination image, with repercussions on activities undertaken by tourists and relevant implications for local policies.

Short bio

Laura Vici is Associate Professor of Applied Economics at the Department of Statistics, University of Bologna (Italy) and member of the Center for Advanced Studies in Tourism. She is member of the Executive Council of the International Association for Tourism Economics. She works on different themes in applied economics, cultural economics, tourism economics and industrial organization. She has been involved in several national and European projects concerning the tourism and the cultural sectors from economic perspectives.