A Study on the Awareness of Destination Image Using Social Network Analysis based on Big Data

Abstract

This study aims to extract meaningful information through a social network analysis based on Big data in order to investigate if a tourist’s awareness of destination image favorably affects competitiveness of tourist destination. A web crawler was employed to gather online review data from 117 destinations and tourism products in Seoul, Korea using Tripadvisor. This web crawler is representative of the online travel community used throughout global tourism industry. Specifically, 23 image factors and 200 components composing two aspects of cognitive and affective images were derived using a system of text mining analysis. This study tried to identify and enhance destination competitiveness by investigating a tourist’s awareness in terms of their destination image. This was measured using indicators of network analysis that were degree centrality, closeness centrality, between centrality and eigenvector centrality. The following results were found: First) 7 image components help to create a positive image of tourist awareness in terms of destination image. Second) 14 image components feel psychologically close to tourists in terms of destination image. Third) 8 image components favorable influence and enhance the competitiveness of destination. Fourth) 11 image components have a greater impact greater than the others when tourists have a perceived destination image. Implications and suggestions are presented along with the findings of the study, which will contribute to the theoretical framework by suggesting a new perspective for measuring destination image based on Big data.