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Can public social media data be harnessed to predict COVID-19 case counts? Weanalyzed approximately 15 million COVID-19 related posts on Weibo, a popularTwitter-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 diagnosisrelated to COVID-19. We then modeled the predictive power of sick posts andother COVID-19 posts on daily case counts. We found that reports of symptomsand diagnosis of COVID-19 significantly predicted daily case counts, up to 14days ahead of official statistics. But other COVID-19 posts did not havesimilar predictive power. For a subset of geotagged posts (3.10% of allretrieved posts), we found that the predictive pattern held true for both Hubeiprovince and the rest of mainland China, regardless of unequal distribution ofhealthcare resources and outbreak timeline. Researchers and disease controlagencies should pay close attention to the social media infosphere regardingCOVID-19. On top of monitoring overall search and posting activities, it iscrucial to sift through the contents and efficiently identify true signals fromnoise.

jmir. journal of medical internet research/journal of medical internet research

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media  to Predict COVID-19 Case Counts: Observational Infoveillance Study in  Mainland China