@文章{信息:doi/10.2196/30753，作者=“ElSherief, Mai和Sumner, Steven A和Jones, Christopher M和Law, Royal K和Kacha-Ochana, Akadia和Shieber, Lyna和Cordier, LeShaundra和Holton, Kelly和De Choudhury, Munmun”，标题=“描述和识别与阿片类药物使用障碍有关的网络错误信息的流行:，期刊="J Med Internet Res"，年="2021"，月="12"，日="22"，卷="23"，数="12"，页数="e30753"，关键词="阿片类药物使用障碍;物质使用;成瘾治疗;错误信息;社交媒体;机器学习;背景:扩大阿片类药物使用障碍(mod)的药物获取和使用是过量预防的关键组成部分。接受mod的一个重要障碍是在个人通常寻求信息的社交媒体或基于网络的论坛上接触到不准确和可能有害的健康错误信息。非常需要设计计算技术来描述与mod相关的基于网络的健康错误信息的流行情况，以促进缓解工作。 Objective: By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. Methods: The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder--related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post's language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. Results: Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91{\%} and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4{\%} on web-based health communities to 0.9{\%} on Twitter. Conclusions: This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment. ", issn="1438-8871", doi="10.2196/30753", url="//www.mybigtv.com/2021/12/e30753", url="https://doi.org/10.2196/30753", url="http://www.ncbi.nlm.nih.gov/pubmed/34941555" }