Text mining analysis of newspaper editorials concerning the COVID-19 pandemic from a healthcare perspective.

Abstract

Objective: This pilot study aimed to examine the content of Japanese newspaper editorials concerning the coronavirus disease 2019 (COVID-19) pandemic and its change over time using text mining analysis. Materials and Methods: The authors analyzed qualitative data from the editorials of five national and 12 regional newspapers on April 7 and 8, 2020 (first state of emergency) and January 8, 2021 (second state of emergency). All analyses were conducted using KH Coder version 3. Results: The co-occurrence network showed a low level of content diversity and a high degree of politicization in the COVID-19 news coverage. The top five high frequency words from the newspapers were "infection", "declaration", "healthcare", "government", and "emergency" at the first state of emergency, and were "declaration", "measures", "government", and "restaurant" at the second one. Conclusion: The results suggest a lack of detailed information and recommendations concerning the public health challenges of the COVID-19 pandemic in Japanese newspaper editorials, even one year after the first wave of the pandemic. This study provides a data-driven foundation for the effectiveness of newspapers in COVID-19 public health communications. The extent to which the quantity and quality of information from newly emerging communication channels, such as social media, influences public understanding of public health measures remains to be established.