Background: The emergence of SARS-CoV-2 (ie, COVID-19) has given rise to a global pandemic affecting 215 countries and over 40 million people as of October 2020. Meanwhile, we are also experiencing an infodemic induced by the overabundance of information, some accurate and some inaccurate, spreading rapidly across social media platforms. Social media has arguably shifted the information acquisition and dissemination of a considerably large population of internet users toward higher interactivities. Objective: This study aimed to investigate COVID-19-related health beliefs on one of the mainstream social media platforms, Twitter, as well as potential impacting factors associated with fluctuations in health beliefs on social media. Methods: We used COVID-19-related posts from the mainstream social media platform Twitter to monitor health beliefs. A total of 92,687,660 tweets corresponding to 8,967,986 unique users from January 6 to June 21, 2020, were retrieved. To quantify health beliefs, we employed the health belief model (HBM) with four core constructs: perceived susceptibility, perceived severity, perceived benefits, and perceived barriers. We utilized natural language processing and machine learning techniques to automate the process of judging the conformity of each tweet with each of the four HBM constructs. A total of 5000 tweets were manually annotated for training the machine learning architectures. Results: The machine learning classifiers yielded areas under the receiver operating characteristic curves over 0.86 for the classification of all four HBM constructs. Our analyses revealed a basic reproduction number R0 of 7.62 for trends in the number of Twitter users posting health belief–related content over the study period. The fluctuations in the number of health belief–related tweets could reflect dynamics in case and death statistics, systematic interventions, and public events. Specifically, we observed that scientific events, such as scientific publications, and nonscientific events, such as politicians’ speeches, were comparable in their ability to influence health belief trends on social media through a Kruskal-Wallis test (P=.78 and P=.92 for perceived benefits and perceived barriers, respectively). Conclusions: As an analogy of the classic epidemiology model where an infection is considered to be spreading in a population with an R0 greater than 1, we found that the number of users tweeting about COVID-19 health beliefs was amplifying in an epidemic manner and could partially intensify the infodemic. It is “unhealthy” that both scientific and nonscientific events constitute no disparity in impacting the health belief trends on Twitter, since nonscientific events, such as politicians’ speeches, might not be endorsed by substantial evidence and could sometimes be misleading.

This is the abstract only. Read the full article on the JMIR site. JMIR is the leading open access journal for eHealth and healthcare in the Internet age.

中文翻译：

---

使用推文了解社交媒体时代与 COVID-19 相关的健康信念如何受到影响：Twitter 数据分析研究

背景：截至 2020 年 10 月，SARS-CoV-2（即 COVID-19）的出现引发了一场全球大流行，影响了 215 个国家和超过 4000 万人。信息，有些准确，有些不准确，在社交媒体平台上迅速传播。社交媒体可以说已经将大量互联网用户的信息获取和传播转向更高的交互性。目的：本研究旨在调查主流社交媒体平台之一 Twitter 上与 COVID-19 相关的健康信念，以及与社交媒体上的健康信念波动相关的潜在影响因素。方法：我们使用来自主流社交媒体平台 Twitter 的 COVID-19 相关帖子来监测健康信念。从 2020 年 1 月 6 日到 6 月 21 日，共检索到 92,687,660 条推文，对应 8,967,986 个独立用户。为了量化健康信念，我们采用了具有四个核心结构的健康信念模型 (HBM)：感知易感性、感知严重性、感知益处和感知障碍。我们利用自然语言处理和机器学习技术来自动化判断每条推文与四个 HBM 结构中的每一个的一致性的过程。总共有 5000 条推文被手动注释，用于训练机器学习架构。结果：机器学习分类器产生的接收器操作特征曲线下面积超过 0.86，用于对所有四种 HBM 结构进行分类。我们的分析显示基本再生数 R0 为 7。62 在研究期间发布与健康信念相关的内容的 Twitter 用户数量趋势。与健康信念相关的推文数量的波动可以反映病例和死亡统计、系统干预和公共事件的动态。具体来说，我们观察到，通过 Kruskal-Wallis 检验，科学事件（例如科学出版物）和非科学事件（例如政治家的演讲）在影响社交媒体上的健康信念趋势的能力方面具有可比性（P=.78 和 P= .92 分别用于感知利益和感知障碍）。结论：作为经典流行病学模型的类比，其中认为感染在 R0 大于 1 的人群中传播，我们发现，发布有关 COVID-19 健康信念的推文的用户数量正在以流行的方式增加，并可能部分加剧信息流行。科学和非科学事件在影响 Twitter 上的健康信念趋势方面没有差异是“不健康的”，因为非科学事件，例如政治家的演讲，可能没有得到实质性证据的支持，有时可能会产生误导。

这只是摘要。阅读 JMIR 网站上的完整文章。JMIR 是互联网时代电子健康和医疗保健领域领先的开放获取期刊。