eDarkTrends: Harnessing Social Media Trends in Substance use disorders for Opioid Listings on Cryptomarket

TL;DR

This paper analyzes social media posts related to opioids on crypto markets using deep learning to understand user perceptions, emotions, and topics, aiming to inform policies for intervention and overdose prevention.

Contribution

It introduces a novel approach combining social media analysis, deep learning, and ontology to assess public sentiment and topics related to opioids on cryptomarkets.

Findings

Identifies drugs associated with positive or negative sentiments.

Correlates topics with emotional responses to different opioids.

Provides insights for targeted policy interventions.

Abstract

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 misuse posts on social media with the opioids being sold through crypto market listings. We use the Drug Abuse Ontology, state-of-the-art deep learning, and BERT-based models to generate sentiment and emotion for the social media posts to understand user perception 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 thankful about or which drug people think negatively about or which opioids cause little to no sentimental reaction. We also perform topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people's responses to various drugs. Our findings can help shape policy to help isolate opioid use cases where timely intervention may be required to prevent adverse consequences, prevent overdose-related deaths, and worsen the epidemic.