School of Biomedical Informatics, The University of Texas Health Science Center at Houston, TX, United States.
Stud Health Technol Inform. 2022 Jun 6;290:962-966. doi: 10.3233/SHTI220223.
The pervasiveness of health information in social media has led to a modern misinformation crisis, also known as a misinfodemic. Misinfodemics have upended public health activities as clearly evident during the COVID-19 pandemic. The objective of this study is to characterize social media content and information sources using theory-driven health behavior and psychology constructs to better understand the motifs of misinformation and their role in the dissemination of health (mis)information in Twitter posts. We analyzed 1,400 randomly selected tweets related to COVID-19 to ascertain four important variables, what is the tweet about (content), how is it structured (linguistic features), who is tweeting (source), and what is the reach of the tweet (dissemination). Results showed there was a significant difference between themes expressed, health beliefs manifested, and observed linguistic patterns in true and false information. Implications for informatics-driven digital health utilities, such as theory-informed knowledge models and context-aware risk communications, are discussed.
社交媒体中健康信息的普及导致了现代错误信息危机,也被称为信息疫情。信息疫情已经颠覆了公共卫生活动,这在 COVID-19 大流行期间表现得尤为明显。本研究的目的是利用理论驱动的健康行为和心理学结构来描述社交媒体内容和信息来源,以更好地理解错误信息的动机及其在 Twitter 帖子中传播健康(错误)信息的作用。我们分析了 1400 条随机选择的与 COVID-19 相关的推文,以确定四个重要变量,即推文的内容、结构(语言特征)、来源(谁在发推)和推文的传播范围(传播)。结果表明,真实信息和虚假信息在表达的主题、表现出的健康信念和观察到的语言模式方面存在显著差异。讨论了信息学驱动的数字健康实用程序的意义,例如基于理论的知识模型和上下文感知风险通信。