Opioid and substance misuse is rampant in the United States today, with the phenomenon known as the "opioid crisis". The relationship between substance use and mental health has been extensively studied, with one possible relationship being: substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means. This study analyzes the substance use posts on social media with opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media posts to understand users' perceptions on social media by investigating questions such as: which synthetic opioids people are optimistic, neutral, or negative about? or what kind of drugs induced fear and sorrow? or what kind of drugs people love or are thankful about? or which drugs people think negatively about? or which opioids cause little to no sentimental reaction. We discuss how we crawled crypto market data and its use in extracting posts for fentanyl, fentanyl analogs, and other novel synthetic opioids. We also perform topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people's responses to various drugs. Additionally, we analyze time-aware neural models built on these features while considering historical sentiment and emotional activity of posts related to a drug. The most effective model performs well (statistically significant) with (macroF1=82.12, recall =83.58) to identify substance use disorder.
翻译:【摘要】
今天在美国,阿片类和药物滥用猖獗,这被称为“阿片类危机”现象。阿片类药物滥用与精神健康问题之间的关系一直被广泛研究,其中一种可能的关系是:药物滥用导致精神健康不佳。然而,缺乏证据导致合法方式获取阿片类药物极其困难。本研究分析了社交媒体上的药物滥用帖子,其中包括通过加密市场销售的阿片类药物。我们使用药物滥用本体论、最先进的深度学习和知识感知的BERT模型,为社交媒体帖子生成情感和情绪,以了解用户对社交媒体上的阿片类药物的看法,并探讨各种阿片类人工合成物引起人们乐观、中立或消极的情绪。另外,我们还分析了与生成的情感和情绪相关的主题,以了解哪些主题与人们对各种药品的反应相关。此外,我们还分析了基于这些特征的时间感知神经模型,同时考虑了与药物相关的帖子的历史情绪和情感活动。最有效的模型表现良好(宏F1=82.12, 召回率=83.58),能够检测药物滥用障碍。