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Opioid and substance misuse is rampant in the United States today, with thephenomenon known as the "opioid crisis". The relationship between substance useand mental health has been extensively studied, with one possible relationshipbeing: substance misuse causes poor mental health. However, the lack ofevidence on the relationship has resulted in opioids being largely inaccessiblethrough legal means. This study analyzes the substance use posts on socialmedia with opioids being sold through crypto market listings. We use the DrugAbuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-basedmodels to generate sentiment and emotion for the social media posts tounderstand users' perceptions on social media by investigating questions suchas: which synthetic opioids people are optimistic, neutral, or negative about?or what kind of drugs induced fear and sorrow? or what kind of drugs peoplelove or are thankful about? or which drugs people think negatively about? orwhich opioids cause little to no sentimental reaction. We discuss how wecrawled crypto market data and its use in extracting posts for fentanyl,fentanyl analogs, and other novel synthetic opioids. We also perform topicanalysis associated with the generated sentiments and emotions to understandwhich topics correlate with people's responses to various drugs. Additionally,we analyze time-aware neural models built on these features while consideringhistorical sentiment and emotional activity of posts related to a drug. Themost effective model performs well (statistically significant) with(macroF1=82.12, recall =83.58) to identify substance use disorder.

"Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb