Global perceptions of South Korea's COVID-19 policy responses: Topic modeling with tweets

Abstract

Abstract This article focuses on South Korea as a case, analyzes a collection of 87,487 tweets referencing both COVID-19 and South Korea during the period of the pandemic, and examines global users’ understandings and/or assessments of South Korean responses to the health crisis. This article uses Pseudo-document-based Topic Model (PTM) as an advanced machine learning technique for classifying short texts into viable topics or themes. In the PTM results, human rights-related topics received much less attention than other topics on government responses, health measures, vaccines, and economic issues. Furthermore, discussions on surveillance, restrictions on assembly, and stigmatization of religious groups tended to emerge rather briefly and soon subsided. Rights protection in the South Korean context appeared at odds with the larger target of protecting public health and the safety of society. The analyses demonstrate a tradeoff between implementing public health imperatives and respecting human rights in South Korea.