Department of Healthcare Research and Policy, University of California, San Diego - Extension, La Jolla, CA, United States.
Global Health Policy and Data Institute, San Diego, CA, United States.
JMIR Public Health Surveill. 2020 Dec 7;6(4):e24125. doi: 10.2196/24125.
The 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.
This 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.
We 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.
We 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.
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, and behaviors about COVID-19, and have the potential to inform future outbreak communication, response, and policy making in China and beyond.
COVID-19 大流行已在全球范围内达到 4000 万例确诊病例。鉴于其快速发展,重要的是要研究其起源,以更好地了解人们的知识、态度和反应随时间的演变。一种方法是使用社交媒体对话数据挖掘来研究信息暴露和用户自我报告的经验。
本研究旨在通过分析中文新浪微博平台上的数据,描述疫情爆发初始中心的社交媒体用户的知识、态度和行为。
我们使用网络爬虫从 2019 年 12 月 31 日至 2020 年 1 月 20 日收集来自武汉市的与 COVID-19 相关关键词的公开微博帖子。然后,我们使用归纳内容编码方法手动注释所有帖子,以识别特定的信息来源和关键主题,包括有关疫情的新闻和知识、公众情绪以及对控制和应对措施的公众反应。
我们从 8703 位唯一的微博用户中识别出 10159 个 COVID-19 帖子。在我们的三个主要分类领域中,67.22%(n=6829)包括新闻和知识帖子,69.72%(n=7083)包括公众情绪,47.87%(n=4863)包括公众反应和自我报告的行为。这些主题中的许多主题都在同一个微博帖子中同时表达。新闻和知识帖子的子主题遵循四个不同的时间线,随着更多信息的出现,表明疫情的严重性不断升级。公众情绪主要集中在焦虑的表达上,但也检测到一些愤怒甚至积极的情绪。公众反应包括保护和增加健康风险行为。
在 2019 年 12 月下旬宣布肺炎和不明原因呼吸道疾病以及 2020 年 1 月 20 日发现人与人之间传播之间,我们观察到公众对 COVID-19 的高度焦虑和困惑,包括用户对新闻的不同反应、在暴露于信息后的负面情绪,以及转化为自我报告行为的公众反应。这些发现为了解 COVID-19 的知识、态度和行为的变化提供了早期的见解,并有可能为中国乃至其他国家的未来疫情传播、应对和政策制定提供信息。