During the COVID-19 pandemic, it has been important to analyze public attention to understand the potential harm of fake news and to direct people toward more reliable sources of information. The current study shares a collection of public Web resources on COVID-19 from Wikipedia, which is one of the most extensive information sources on the Internet. Tracking relevant content can be difficult because page titles may change over time and may not contain any relevant keywords. To address this issue, the research developed a new search method that identifies relevant content more comprehensively than keyword-based searches. This method allows an intuitive categorization of hierarchical topics such as biomedical, people-related, and regional events. A total of 18,492 Wikipedia pages on coronavirus disease 2019 (COVID-19) in 11 languages over 852 days from January 1, 2020, are shared via this research. By correlating the confirmed cases in various countries with the view counts of Wikipedia pages in corresponding languages, we were able to study how information-seeking behavior on Wikipedia changed as the pandemic evolved. This analysis revealed the prevalence of seeking information in Wikipedia with different immediacy depending on language, that as the pandemic progressed, public attention shifted from biomedical information to regional events. Furthermore, Wikipedia's voluntary nature leads to inevitable differences in the scale and depth of information available across the urgent matter among several hundreds of language projects it supports. To study this gap, we analyze a near-complete set of 7,830 articles on COVID-19 written in 15 languages and examine how various information types, particularly on \textit{local} content that is about a specific geographic region, are covered across the studied languages. We also examine the temporal relationship across language projects regarding information propagation. Overall, the findings suggest that there is an increasing need for local information during the pandemic and that additional support is needed to cover this type of content strategically.
이 논문에서는 온라인 지식 제공 플랫폼에서 언어별로 제공하는 정보의 양과 소비 격차에 대해 다루었다. 코로나 범유행을 통해 우리는 가짜뉴스가 얼마나 빠르게 퍼지고 공중보건을 위협하는지 배웠다. 특히, 신뢰할만한 정보를 습득하기 어려운 정보 소외 계층은 범유행에 많은 피해를 입었다. 이 논문은 가짜 뉴스를 막고 정보 격차를 줄이기 위해 새로운 정보 수집법과 정보를 바라보는 새로운 시각을 제안한다. 위키백과에서 2020년 1월 1일부터 2022년 5월1일까지 코로나바이러스 관련 문서 18,492개와 각 문서의 조회수, 수정횟수, 정보량 등을 수집하였다. 문서의 내용에 따라 문서의 종류를 분류하고 Cross-Correlation coefficient, Bow-tie analysis와 같은 자료분석 기법을 적용하여 정보에 대한 대중의 관심과 정보의 흐름, 언어별 정보 격차를 밝혀내었다. 이 논문을 통해 지역 언어로 쓰인 지역 정보의 중요성을 확인할 수 있고 정보 격차를 줄이기 위해 정보 생성의 가이드라인이 필요하다는 것을 알 수 있다.
