Structural Topic Model Analysis of Mask-Wearing Issue Using International News Big Data

Abstract

Media plays an important role in the acquisition of health information worldwide. This was particularly evident in the face of the COVID-19 epidemic. Relatedly, it is practical and desirable for people to wear masks for health, fashion, and religious regions. However, depending on cultural differences, people naturally accept wearing a mask, or they look upon it negatively. In 2020, the COVID-19 pandemic led to widespread mask-wearing mandates worldwide. In the case of COVID-19, wearing a mask is strongly recommended, so by analyzing the news data before and after the spread of the epidemic, it is possible to see how the direction of crisis management is being structured. In particular, by utilizing big data analysis of international news data, discourses around the world can be analyzed more deeply. This study collected and analyzed 58,061 international news items related to mask-wearing from 1 January 2019 to 31 December 2020. The collected dataset was compared before and after the World Health Organization’s pandemic declaration by applying structural topic model analysis. The results revealed that prior to the declaration, issues related to the COVID-19 outbreak were emphasized, but afterward, issues related to movement restrictions, quarantine management, and local economic impacts emerged.