Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Online Information Sources of Coronavirus Using Webometric Big Data

코로나19 사태와 온라인 정보의 다양성 연구 - 빅데이터를 활용한 글로벌 접근법

  • Park, Han Woo (Interdisciplinary Graduate Programs of Digital Convergence Business and East Asian Cultural Studies, Department of Media and Communication, YeungNam University) ;
  • Kim, Ji-Eun (Cyber Emotions Research Institute, YeungNam University) ;
  • Zhu, Yu-Peng (Cyber Emotions Research Institute, YeungNam University)
  • 박한우 (영남대학교 언론정보학과, 디지털융합비즈니스학 및 동아시아문화학 협동과정 대학원) ;
  • 김지은 (영남대학교 디지털융합비지니스학 대학원, 사이버감성연구소) ;
  • 주우붕 (영남대학교 디지털융합비지니스학 대학원, 사이버감성연구소)
  • Received : 2020.07.13
  • Accepted : 2020.11.06
  • Published : 2020.11.30
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Abstract

Using webometric big data, this study examines the diversity of online information sources about the novel coronavirus causing the COVID-19 pandemic. Specifically, it focuses on some 28 countries where confirmed coronavirus cases occurred in February 2020. In the results, the online visibility of Australia, Canada, and Italy was the highest, based on their producing the most relevant information. There was a statistically significant correlation between the hit counts per country and the frequency of visiting the domains that act as information channels. Interestingly, Japan, China, and Singapore, which had a large number of confirmed cases at that time, were providing web data related to the novel coronavirus. Online sources were classified using an N-tuple helix model. The results showed that government agencies were the largest supplier of coronavirus information in cyberspace. Furthermore, the two-mode network technique revealed that media companies, university hospitals, and public healthcare centers had taken a positive attitude towards online circulation of coronavirus research and epidemic prevention information. However, semantic network analysis showed that health, school, home, and public had high centrality values. This means that people were concerned not only about personal prevention rules caused by the coronavirus outbreak, but also about response plans caused by life inconveniences and operational obstacles.

이 연구는 웹보메트릭 빅데이터를 활용하여 코로나바이러스 확진 국가(지역)들의 온라인 정보원의 다양성을 조사했다. 구체적으로 2020년 2월에 코로나바이러스 확진자가 발생한 28개국을 대상으로 웹 데이터를 수집한 결과, 호주, 캐나다, 이탈리아 등의 온라인 가시성이 높게 나타나면서 관련 정보를 가장 많이 생산하고 있었다. 국가별 검색건수(hit counts)와 정보채널의 역할을 하는 도메인(domain) 빈도와는 통계적으로 유의한 상관성이 있었다. 한편 데이터 수집도구인 bing.com의 점유률이 평소에도 높은 국가들을 제외하고 다시 검토한 결과, 당시 확진자 수가 많았던 일본, 중국, 싱가포르 등이 코로나바이러스와 관련된 웹데이터를 주도적으로 올리고 있었다. 온라인 정보원은 n-헬릭스를 활용하여 분류되었다. n-헬릭스는 대학-기업-정부의 3주체간 상호작용과 혁신을 강조하는 트리플헬릭스모델(Triple Helix Model)에 기반한 확장된 분석틀이다. 그 결과, 정부기관이 18.1%를 차지하면서 코로나바이러스 정보의 최대 공급자로 나타났다. 2원성 네트워크 분석결과를 보면 언론사, 대학병원, 공중보건에 특화된 조직 등도 코로나바이러스 연구와 방역 정보의 온라인 유통에 적극적이었다. 웹페이지에 포함된 단어들을 중심으로 내용분석을 해 보니 건강, 학교, 가족, 공공, 방안 등의 단어가 중심성이 높게 나타나 코로나바이러스로 인한 개인별 예방수칙뿐만 아니라 생활 불편과 업무장애로 인한 대처방안 등에 관심이 높다는 것을 알 수 있었다.

Keywords

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