Traveller‐generated destination image: Analysing Flickr photos of 193 countries worldwide. (30th September 2020)

• Record Type: Journal Article
• Authors: Taecharungroj, Viriya; Mathayomchan, Boonyanit
• Is Part Of: International journal of tourism research. Volume 23:Number 3(2021)
• Page Start: 417
• Page End: 441
• Publication Date: 2020-09-30
• Subjects: destination image -- Flickr -- Google Cloud Vision AI -- image analysis -- latent Dirichlet allocation; Tourism -- Periodicals; 338.4791
• DOI: 10.1002/jtr.2415
• Languages: English
• ISSNs: 1099-2340

Abstract

Abstract: The purpose of this research is to introduce a method that utilises a combination of Google Cloud Vision AI's label detection and a topic‐modelling algorithm, latent Dirichlet allocation, to identify common destination images and to compare destinations worldwide. The study analyses 283, 912 photos of 193 countries from Flickr.com, and 16 cognitive image attributes (CIAs) are identified. Subsequent hotspot analyses indicate the exact locations of these CIAs in three sample countries: France, the US, and Thailand. Destination marketing organisations (DMOs) can use this method to more effectively analyse and promote destinations during and after the COVID‐19 pandemic.