Sato Ryuya, Tsuchiya Masami, Ichiyama Rintaro, Hisamura Soma, Watabe Satoshi, Yanagisawa Yuki, Nishiyama Tomohiro, Yada Shuntaro, Aramaki Eiji, Kizaki Hayato, Imai Shungo, Hori Satoko
Division of Drug Informatics, Keio University Faculty of Pharmacy.
Division of Information Science, Nara Institute of Science and Technology.
Yakugaku Zasshi. 2024;144(12):1125-1135. doi: 10.1248/yakushi.24-00154.
Intentional overdose (OD) of over-the-counter (OTC) and prescription drugs is becoming a significant social issue all over the world. While previous research has focused on drug misuse, there has been limited analysis using social networking service data. This study aims to analyze posts related to a drug overdose on Twitter (X) to understand the characteristics and trends of drug misuse, and to examine the applicability of social media in understanding the current situation of OD through natural language processing techniques. We collected posts in Japanese containing the term "OD" from January 10 to February 8, 2023, and analyzed 30203 posts. Using a pre-trained, fine-tuned bidirectional encoder representations from transformers (BERT) model, we classified the posts into categories, including direct mentions of OD. We examined the content for drug types and emotional context. Among the 5283 posts categorized as "Posts describing ODing," about one-third included specific drug names or related terms. The most frequently mentioned OTC drugs included active ingredients such as codeine, dextromethorphan, ephedrine, and diphenhydramine. Prescription drugs, particularly benzodiazepines and pregabalin, were also common. Tweets peaked at midnight, suggesting a link between negative emotions and potential OD incidents. Our classifier showed high accuracy in distinguishing OD-related posts. Analyzing Twitter posts provides valuable insights into the patterns and emotional contexts of drug misuse. Monitoring social networking services for OD-related content could help identify high-risk individuals and inform prevention strategies. Enhanced monitoring and public awareness are crucial to reducing the risks associated with both OTC and prescription drug misuse.
非处方（OTC）药和处方药的故意过量服用（OD）正在成为全球一个重大的社会问题。虽然先前的研究主要集中在药物滥用方面，但利用社交网络服务数据进行的分析有限。本研究旨在分析推特（X）上与药物过量服用相关的帖子，以了解药物滥用的特征和趋势，并通过自然语言处理技术检验社交媒体在了解药物过量服用现状方面的适用性。我们收集了2023年1月10日至2月8日期间包含术语“OD”的日语帖子，并分析了30203条帖子。使用预训练、微调的双向编码器表征来自变换器（BERT）模型，我们将帖子分类，包括直接提及药物过量服用的内容。我们检查了帖子中的药物类型和情感背景。在被归类为“描述药物过量服用的帖子”的5283条帖子中，约三分之一包含特定的药物名称或相关术语。最常提及的非处方药包括可待因、右美沙芬、麻黄碱和苯海拉明等活性成分。处方药，特别是苯二氮䓬类药物和普瑞巴林也很常见。推文在午夜达到峰值，表明负面情绪与潜在的药物过量服用事件之间存在联系。我们的分类器在区分与药物过量服用相关的帖子方面显示出很高的准确性。分析推特帖子为药物滥用的模式和情感背景提供了有价值的见解。监测社交网络服务中与药物过量服用相关的内容有助于识别高危个体并为预防策略提供信息。加强监测和公众意识对于降低非处方药和处方药滥用相关风险至关重要。
