Characterizing Weibo Social Media Posts From Wuhan, China During the Early Stages of the COVID-19 Pandemic: Qualitative Content Analysis

"...hope that these results can help inform governments and public health stakeholders on strategies to improve outbreak communication for COVID-19 and into the future, in an era where digital platforms are now a dominant source of information and interaction."

Data derived from social media platforms can be collected and analysed to gauge the public's knowledge, attitudes, and behaviour in close to real time. This infoveillance study focuses on China's Sina Weibo platform to understand the communication patterns of social media users located at the initial epicentre of the COVID-19 pandemic (Wuhan, China). Though the data were collected in early days of the outbreak, the information that is revealed could be used to prepare for re-emergence or new waves of COVID-19 in different communities, ensure appropriate health messaging on new COVID-19 developments (e.g., vaccines), and communicate to the public about ongoing social distancing, masking, handwashing, and reopening recommendations.

Weibo permits users to post up to 2,000 characters with or without images, videos, and other multimedia, and users may repost messages. These characteristics (particularly the high character count compared to other microblogging platforms) permits a user's post to address multiple topics and have a rich qualitative discussion of issues. The researchers used web scraping to collect public Weibo posts from December 31 2019 to January 20 2020 from users located in Wuhan City that contained COVID-19-related keywords. They used an inductive content coding approach to identify specific information sources and key themes.

Through this process, the researchers identified 10,159 COVID-19 posts from 8,703 unique Weibo users. Among 3 parent classification areas, 67.22% (n=6,829) included news and knowledge posts, 69.72% (n=7,083) included public sentiment, and 47.87% (n=4,863) included public reaction to control and response measures and self-reported behaviour. Many of these themes were expressed concurrently in the same Weibo post. Notably,the researchers found some information that can be categorised as misinformation. For example, they detected posts suggesting that using BanLanGen (a traditional medicine) could prevent COVID-19 infection, even though there was no scientific basis at the time for this claim.

Subclassification of topics within the parent classifications were based on the Health Belief Model, where constructs of perceived susceptibility and severity, and cues for action, were explored for user-generated posts. The subtopics for news and knowledge posts followed 4 distinct timelines and evidenced an escalation of the outbreak's seriousness as more information became available. Public sentiment primarily focused on expressions of anxiety, though some expressions of anger and even positive sentiment (e.g., those who reported being satisfied with the level of transparency of the Chinese government and its outbreak response) were also detected. Public reaction included both protective (e.g., wearing masks) and elevated health risk behaviour (e.g., self-treatment with unproven therapy and nutritional products).

Thus, this study found that "the exposure to changing news and information on Weibo and the ability of these social media users to directly communicate their opinions and behaviors evidenced the complex interaction between the government, media sources, and the public during an early outbreak period." The topics that were discussed changed over time as new information about COVID-19 emerged and was communicated to the public. For the researchers, the relationship between exposure to news and information revealed by these results coheres with agenda-setting theory, which "describes how the media stimulates awareness, shapes and filters reality, and sets priorities of the public for salient issues including for public health concerns....Future work should focus on further adapting the agenda-setting theory to health promotion efforts targeted for outbreak response and that is contextualized for local communities and social media platforms..."

Arguing that the findings have the "potential to inform future outbreak communication, response, and policy making in China and beyond," the researchers conclude: "Analyzing social media data can provide valuable insights into a communities' knowledge, concerns, and fears, which can influence individual and population-level behavior - important factors that can have a direct impact on the success or failure of public health interventions aimed at containing the spread of a disease. These findings can also aid in developing communication tools and health promotion activities to help the public better understand transmission risks, correct confusion or misinformation, and educate on social and behavioral risks that may exacerbate spread."