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.

本文使用深度学习、BERT 模型以及情感分析来研究社交媒体中与合成类阿片类药物相关的帖子，分析了社交媒体和加密市场数据，探究人们对不同药物的感受和态度，最终建立了基于这些特征的时间感知神经模型，成功地 (具有统计学意义) 识别出物质滥用障碍。