Background Coronavirus disease (COVID-19) has affected more than 200 countries and territories worldwide. This disease poses an extraordinary challenge for public health systems because screening and surveillance capacity is often severely limited, especially during the beginning of the outbreak; this can fuel the outbreak, as many patients can unknowingly infect other people. Objective The aim of this study was to collect and analyze posts related to COVID-19 on Weibo, a popular Twitter-like social media site in China. To our knowledge, this infoveillance study employs the largest, most comprehensive, and most fine-grained social media data to date to predict COVID-19 case counts in mainland China. Methods We built a Weibo user pool of 250 million people, approximately half the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19–related posts from our user pool from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify “sick posts,” in which users report their own or other people’s symptoms and diagnoses related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China. Results We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China regardless of the unequal distribution of health care resources and the outbreak timeline. Conclusions Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. In addition to monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understanding of information sharing behaviors is a promising approach to identify true disease signals and improve the effectiveness of infoveillance.

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The past nine months witnessed COVID-19's fast-spreading at the global level. Limited by medical resources shortage and uneven facilities distribution, online help-seeking becomes an essential approach to cope with public health emergencies for many ordinaries. This study explores the driving forces behind the retransmission of online help-seeking posts. We built an analytical framework that emphasized content characteristics, including information completeness, proximity, support seeking type, disease severity, and emotion of helpseeking messages. A quantitative content analysis was conducted with a probability sample consisting of 727 posts. The results illustrate the importance of individual information completeness, high proximity, instrumental support seeking. This study also demonstrates slight inconformity with the severity principle but stresses the power of anger in help-seeking messages dissemination. As one of the first online help-seeking diffusion analyses in the COVID-19 period, our research provides a reference for constructing compelling and effective help-seeking posts during a particular period. It also reveals further possibilities for harnessing social media's power to promote reciprocal and cooperative actions as a response to this deepening global concern.

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Not only did COVID-19 give rise to a global pandemic, but also it resulted in an infodemic comprising misinformation, rumor, and propaganda. The consequences of this infodemic can erode public trust, impede the containment of the virus, and outlive the pandemic itself. The evolving and fragmented media landscape, particularly the extensive use of social media, is a crucial driver of the spread of misinformation. Focusing on the Chinese social media Weibo, we collected four million tweets, from December 9, 2019, to April 4, 2020, examining misinformation identified by the fact-checking platform Tencent-a leading Chinese tech giant. Our results show that the evolution of misinformation follows an issue-attention cycle pertaining to topics such as city lockdown, cures and preventive measures, school reopening, and foreign countries. Sensational and emotionally reassuring misinformation characterizes the whole issue-attention cycle, with misinformation on cures and prevention flooding social media. We also study the evolution of sentiment and observe that positive sentiment dominated over the course of Covid, which may be due to the unique characteristic of "positive energy" on Chinese social media. Lastly, we study the media landscape during Covid via a case study on a controversial unproven cure known as Shuanghuanglian, which testifies to the importance of scientific communication in a plague. Our findings shed light on the distinct characteristics of misinformation and its cultural, social, and political implications, during the COVID-19 pandemic. The study also offers insights into combating misinformation in China and across the world at large.