Abstract
BackgroundThe COVID-19 pandemic has reached 40 million confirmed cases worldwide. Given its rapid progression, it is important to examine its origins to better understand how people’s knowledge, attitudes, and reactions have evolved over time. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences.ObjectiveThis study aims to characterize the knowledge, attitudes, and behaviors of social media users located at the initial epicenter of the outbreak by analyzing data from the Sina Weibo platform in Chinese.MethodsWe 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. We then manually annotated all posts using an inductive content coding approach to identify specific information sources and key themes including news and knowledge about the outbreak, public sentiment, and public reaction to control and response measures.ResultsWe identified 10,159 COVID-19 posts from 8703 unique Weibo users. Among our three parent classification areas, 67.22% (n=6829) included news and knowledge posts, 69.72% (n=7083) included public sentiment, and 47.87% (n=4863) included public reaction and self-reported behavior. Many of these themes were expressed concurrently in the same Weibo post. Subtopics for news and knowledge posts followed four 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 were also detected. Public reaction included both protective and elevated health risk behavior.ConclusionsBetween the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior. These findings provide early insight into changing knowledge, attitudes, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.
Highlights
First documented in December 2019, the novel coronavirus is thought to have originated from the city of Wuhan in Hubei Province, China and has quickly emerged as the greatest global public health threat in the last century while representing a significant test for global preparedness to prevent, diagnose, treat, and contain a highly transmittable disease
Between the announcement of pneumonia and respiratory illness of unknown origin in late December 2019 and the discovery of human-to-human transmission on January 20, 2020, we observed a high volume of public anxiety and confusion about COVID-19, including different reactions to the news by users, negative sentiment after being exposed to information, and public reaction that translated to self-reported behavior
These findings provide early insight into changing knowledge, attitudes, http://publichealth.jmir.org/2020/4/e24125/
Summary
First documented in December 2019, the novel coronavirus is thought to have originated from the city of Wuhan in Hubei Province, China and has quickly emerged as the greatest global public health threat in the last century while representing a significant test for global preparedness to prevent, diagnose, treat, and contain a highly transmittable disease. With the COVID-19 outbreak originating and spreading from China, notable given the country’s large population, high density of Hubei Province, and the outbreak coinciding with the Lunar New Year period, China implemented a significant public health response including mandated quarantines, community and social isolation, and the construction of two new hospitals [5]. Despite these aggressive measures, at the onset of the outbreak, little was known about the structure, etiology, transmission dynamics, and appropriate public health measures needed to curtail the spread of COVID-19. One method is to use data mining of social media conversations related to information exposure and self-reported user experiences
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