背景:关于阿片类药物使用障碍(OUD)治疗的健康错误信息和神话存在于社交媒体上,并为预防药物过量死亡带来了挑战。然而,没有系统的,存在定量方法来识别哪些类型的错误信息正在被分享和讨论。
目标:我们开发了一个多阶段的分析管道来评估来自Twitter(后来更名为X)的社交媒体帖子,YouTube,Reddit,和药物-关于OUD治疗的健康错误信息的论坛。
方法:我们的方法首先使用文档嵌入来识别来自已知神话的错误信息的潜在新陈述。这些陈述使用分层聚集聚类被分组为主题,然后,公共卫生专家审查了错误信息的结果。
结果:我们在上述平台上共收集了19,953,599篇讨论阿片类药物相关内容的帖子。我们的多级分析管道确定了7个主要集群或讨论主题。在供进一步公共卫生专家审查的高收益数据集(n=303)中,其中包括关于OUD的潜在治疗方法的讨论(90/303,29.8%),成瘾的性质(68/303,22.5%),物质的药理特性(52/303,16.9%),注射用药(36/303,11.9%),疼痛和阿片类药物(28/303,9.3%),药物的身体依赖性(22/303,7.2%),曲马多使用量(7/303,2.3%)。公共卫生专家对每个集群中的内容进行了审查,发现存在错误信息和神话,而不是用作种子神话来初始化算法的神话。
结论:通过适当的沟通策略识别和解决错误信息可能是预防过量死亡的一个日益重要的组成部分。为了进一步实现这一目标,我们开发并测试了一种方法,以帮助识别来自大规模社交媒体内容的关于OUD的神话和错误信息。
BACKGROUND: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed.
OBJECTIVE: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD.
METHODS: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation.
RESULTS: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm.
CONCLUSIONS: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content.