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http://dx.doi.org/10.5762/KAIS.2020.21.7.475

Identifying Regional Tourism Resources Using Webometric Network Analysis: A case of Suseong-gu in Daegu, South Korea  

Song, Hwa Young (Interdisciplinary Program of Digital Convergence Business, Yeung-nam University)
Zhu, Yu Peng (Interdisciplinary Program of Digital Convergence Business, Yeung-nam University)
Kim, Ji Eun (Interdisciplinary Program of Digital Convergence Business, Yeung-nam University)
Oh, Jung Hyun (SOCE Inc.)
Park, Han Woo (Interdisciplinary Program of Digital Convergence Business, Department of Media and Communication, Yeung-nam University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.7, 2020 , pp. 475-486 More about this Journal
Abstract
The purpose of present study is to identify the regional tourism resources using Webometric network analysis. The study focuses on Suseong area in Daegu metropolitan city. Various kinds of web-based data, for example, hit counts, online news, and public comments, were used to discover hot places and people's responses. The research question is, 'First, what is the optimum level of the search engine for suseong? Second, what is the online appearance of tourist resources in suseong? Which region is the center of tourism with high levels of emergence? Third, what are the main contents of news articles and comments related to the Suseong pond?'. The results show that the search engine optimization level in Suseong is lower than that in other areas in Daegu. In other words, tourism information and contents regarding Suseong are not highly visible on cyber space. Importantly, Suseong pond had the highest online presence. A close analysis of both online news and users' comments on Suseong pond, however, revealed the biggest concern as calling for improving public accessibility to tourism infrastructure. The findings are expected to contribute to policy development and service operation related to tourism resources in Suseong.
Keywords
Daegu Suseong-gu; Tourism; Webometrics; Network Analysis; Naver News; Comment Analysis;
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Times Cited By KSCI : 4  (Citation Analysis)
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