Reddit 上阿片类药物使用障碍相关在线问题的大规模分析
Large-Scale Analysis of Online Questions Related to Opioid Use Disorder on Reddit
AI总结：
本文通过 Transformer 问题检测与层次聚类，对 Reddit 上 19 个 subreddit 中 2018—2021 年的 OUD 相关问题进行大规模分析，识别出 6 个粗粒度、69 个细粒度类别及十大信息寻求领域，为理解公众隐性信息需求并设计减害干预提供依据。
摘要：
阿片类药物使用障碍（OUD）是一个主要的健康问题，既影响个人福祉，也影响整体公共健康。由于多种原因，包括使用阿片类药物者所面临的污名化，人们在不同的社交媒体平台上形成了用于康复和支持的在线社区。在这些社区中，人们分享自身经历，并通过提问来获取有关阿片类药物使用与康复的信息。然而，这些社区并不总是包含经过临床验证的信息。本文研究了在 Reddit 上 OUD 相关讨论语境中提出的自然语言问题。我们采用基于 Transformer 的问题检测方法，并结合跨 19 个 subreddit 的层次聚类，识别出 6 个粗粒度类别和 69 个细粒度类别的 OUD 相关问题。我们的分析揭示了在 2018—2021 年研究期间，Reddit 用户在 OUD 语境下信息寻求的十个领域：药物销售、特定药物相关问题、OUD 治疗、药物使用、副作用、戒断、生活方式、药物检测、疼痛管理及其他。我们的工作为更好地理解人们在 Reddit 上以不引人注目的方式提出的 OUD 相关问题迈出了重要一步。最后，我们基于这些问题的主题讨论了技术干预措施和公共卫生减害技术。
英文摘要：
Opioid use disorder (OUD) is a leading health problem that affects individual well-being as well as general public health. Due to a variety of reasons, including the stigma faced by people using opioids, online communities for recovery and support were formed on different social media platforms. In these communities, people share their experiences and solicit information by asking questions to learn about opioid use and recovery. However, these communities do not always contain clinically verified information. In this paper, we study natural language questions asked in the context of OUD-related discourse on Reddit. We adopt transformer-based question detection along with hierarchical clustering across 19 subreddits to identify six coarse-grained categories and 69 fine-grained categories of OUD-related questions. Our analysis uncovers ten areas of information seeking from Reddit users in the context of OUD: drug sales, specific drug-related questions, OUD treatment, drug uses, side effects, withdrawal, lifestyle, drug testing, pain management and others, during the study period of 2018-2021. Our work provides a major step in improving the understanding of OUD-related questions people ask unobtrusively on Reddit. We finally discuss technological interventions and public health harm reduction techniques based on the topics of these questions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
