  Can public social media data be harnessed to predict COVID-19 case counts? We
analyzed approximately 15 million COVID-19 related posts on Weibo, a popular
Twitter-like social media platform in China, from November 1, 2019 to March 31,
2020. We developed a machine learning classifier to identify "sick posts,"
which are reports of one's own and other people's symptoms and diagnosis
related to COVID-19. We then modeled the predictive power of sick posts and
other COVID-19 posts on daily case counts. We found that reports of symptoms
and diagnosis of COVID-19 significantly predicted daily case counts, up to 14
days ahead of official statistics. But other COVID-19 posts did not have
similar predictive power. For a subset of geotagged posts (3.10% of all
retrieved posts), we found that the predictive pattern held true for both Hubei
province and the rest of mainland China, regardless of unequal distribution of
healthcare resources and outbreak timeline. Researchers and disease control
agencies should pay close attention to the social media infosphere regarding
COVID-19. On top of monitoring overall search and posting activities, it is
crucial to sift through the contents and efficiently identify true signals from
noise.
