Background: Masks are well accepted in Japan, where they were already part of daily life even before the COVID-19 pandemic. Unlike many other countries where mask mandates were lifted as soon as the pandemic was under control, Japan was one of the last countries to ease mask-wearing guidelines. Even after the formal announcement to allow masks-off in mid-March 2023, many Japanese still voluntarily wear masks. In this work, possible reasons for this extreme "mask-affinity" of Japanese people were studied by exploring various information sources including tweets (now known as X posts) and subsequent text-analysis, online news, and medical literature.

Methods: An observational study was conducted based on tweets prospectively collected during 5 months from June 26, 2022 to November 26, 2022. Tweets with the hashtag "mask (in Japanese)" were collected weekly via the Twitter application programming interface by using R version 4.0.3 to gauge public opinions. The word clouds to allow intuitive understanding of the key words were drawn from the tokenized text.

Results: The data collection period included the 7 flareups of the newly infected cases i.e. "the 7 surge". In total, 161,592 tweets were collected. Word clouds for 1) before the 7 surge based on 18,000 tweets on June 26 and 2) during/after the 7 surge based on 143,592 tweets between July-November were created with the R package "wordcloud2". The results indicated that the people wanted to take off masks due to the heat in summer, then shifted again toward mask-wearing along with the 7 surge but with a certainly growing "no-mask" sentiment.

Conclusions: Subsequent review of domestic information sources suggested that various factors, not only well-known peer pressure, may have contributed the public's mask affinity in Japan. This work revealed an aspect of Japanese struggle toward adaptation to life in an unexpected pandemic by focusing on masks as our closest daily adjunct over the past 3 years of isolation.Trial registration: not applicable.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10953900PMC
http://dx.doi.org/10.1016/j.dialog.2024.100172DOI Listing

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