Since the outbreak of coronavirus disease 2019 (COVID-19) in the late 2019,
it has affected over 200 countries and billions of people worldwide. This has
affected the social life of people owing to enforcements, such as "social
distancing" and "stay at home." This has resulted in an increasing interaction
through social media. Given that social media can bring us valuable information
about COVID-19 at a global scale, it is important to share the data and
encourage social media studies against COVID-19 or other infectious diseases.
Therefore, we have released a multilingual dataset of social media posts
related to COVID-19, consisting of microblogs in English and Japanese from
Twitter and those in Chinese from Weibo. The data cover microblogs from January
20, 2020, to March 24, 2020. This paper also provides a quantitative as well as
qualitative analysis of these datasets by creating daily word clouds as an
example of text-mining analysis. The dataset is now available on Github. This
dataset can be analyzed in a multitude of ways and is expected to help in
efficient communication of precautions related to COVID-19.

本研究旨在推广社交媒体研究对抗 COVID-19 及其他传染病。通过对 COVID-19 微博的量化和定性分析，我们提供了多种处理数据集的方法，并分享了一个多语种社交媒体数据集，供研究人员使用。