Danu Kim  Damin Lee  Jaehyeon Myung  Changwook Jung  Inho Hong  Diego Sáez-Trumper  Jinhyuk Yun  Woo-Sung Jung  Meeyoung Cha

10.5626/JOK.2022.49.5.347

Wikipedia COVID-19 Data Collection information structure Network crosslinguistic

Access to accurate information is essential to reduce the social damage caused by the Coronavirus Disease 2019 (COVID-19) pandemic. Information about ongoing events, such as COVID-19, is quickly updated on Wikipedia, an accessible internet encyclopedia that allows users to edit it themselves. However, the existing Wikipedia information retrieval method has a limitation in collecting information, including relationships between documents. The template format of Wikipedia reflects the structure of information as a link that is selectively applied to documents with high relevance. This study collected information on COVID-19 in 10 languages on Wikipedia using a template and reorganized it into networks. Among the 10 networks with 130,662 nodes and 202,258 edges, languages with a large number of active users had a template network with a large size and depth, and documents highly related to COVID-19 existed within a 3-hop connection structure. This research proposed a new information retrieval method applicable to multiple languages and contributes to the construction of document lists related to specific topics.