Columbia University, School of Social Work, New York, NY (NE-B, KRH, MNS); Columbia University, Department of Computer Science, New York, NY (CY, YZ, SM); Columbia University, Data Science Institute, New York, NY (SM).
J Addict Med. 2022;16(2):e123-e132. doi: 10.1097/ADM.0000000000000883.
This paper uses a social media platform, Reddit, to identify real-time experiences of people who use drugs during the COVID-19 lock-down.
Reddit is a popular and growing social media platform, providing a large, publicly available dataset necessary for high performance of machine learning and topic modeling techniques. We used opioid-related "subreddits," communities where Reddit users engage in conversations about drug use, to examine COVID-19-related content of posts and comments from March to May 2020. This paper investigates the latent topics of users' posts/comments using Latent Dirichlet Allocation, an unsupervised machine learning approach that uncovers the thematic structure of a document collection. We also examine how topics changed over time.
The final dataset consists of 525 posts and 9284 comments, for a total of 9809 posts/comments (3756 posts/comments in r/opiates, 1641 in r/OpiatesRecovery, 1203 in r/suboxone, and 3209 in r/Methadone) among 2342 unique individuals. There were 5256 posts/comments in March; 3185 in April; and 1368 in May (until May 22). Topics that appeared most frequently in COVID-19-related discussions included medication for opioid use disorder experiences and access issues (22.6%), recovery (24.2%), and drug withdrawal (20.2%).
During the first three months of the COVID-19 pandemic, people who use drugs were impacted in several ways, including forced or intentional withdrawal, confusion between withdrawal and COVID-19 symptoms, take-home medication for opioid use disorder issues, and barriers to recovery. As the pandemic progresses, providers and policymakers should consider these experiences among people who use drugs during the early stage of the pandemic.
本文利用社交媒体平台 Reddit，识别在 COVID-19 封锁期间使用毒品的人的实时体验。
Reddit 是一个流行且不断发展的社交媒体平台，提供了一个大型的、公开可用的数据集，这是高性能机器学习和主题建模技术所必需的。我们使用与阿片类药物相关的“subreddits”，即 Reddit 用户就药物使用进行对话的社区，来检查 2020 年 3 月至 5 月期间的与 COVID-19 相关的帖子和评论。本文使用潜在狄利克雷分配（Latent Dirichlet Allocation）来研究用户帖子/评论的潜在主题，这是一种无监督机器学习方法，可以揭示文档集合的主题结构。我们还检查了主题随时间的变化情况。
最终数据集包括 525 个帖子和 9284 条评论，总计 9809 个帖子/评论（3756 个帖子/评论在 r/opiates，1641 个在 r/OpiatesRecovery，1203 个在 r/suboxone，3209 个在 r/Methadone），来自 2342 个不同的个体。3 月有 5256 个帖子/评论；4 月有 3185 个；5 月有 1368 个（截至 5 月 22 日）。在与 COVID-19 相关的讨论中出现频率最高的主题包括阿片类药物使用障碍药物治疗体验和获取问题（22.6%）、康复（24.2%）和药物戒断（20.2%）。
在 COVID-19 大流行的头三个月，吸毒者受到了多方面的影响，包括被迫或有意戒断、将戒断症状与 COVID-19 症状混淆、阿片类药物使用障碍药物的带药回家问题，以及康复障碍。随着大流行的进展，在大流行的早期阶段，服务提供者和政策制定者应该考虑吸毒者的这些经历。
