Data-Driven Insights on Opioid Use and Health Behavior Trends Following Decriminalization: Zero-Shot Sentiment and Behavior Analysis
Harfi Moridani, S.; Yang, C.; Noaeen, M.; Shakeri, Z.
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Opioid decriminalization has taken on renewed urgency in regions grappling with high mortality and health-care costs. Traditional assessments often focus on legal or epidemiological data, leaving gaps in understanding how the public actually perceives and reacts to such policies. This paper introduces an AI-driven approach that applies Mistral, a Large Language Model (LLM), to a corpus of over 22,000 Reddit comments discussing British Columbias decriminalization policy. Our method uses zero-shot classification to track shifts in sentiment and self-reported behaviors related to opioid use and harm reduction. The findings suggest that online conversations initially reflected optimism about reduced stigma and broader acceptance of harm reduction measures, but sentiment became more mixed as policy details and lived experiences surfaced. This pattern indicates that advanced LLM-based text analysis can yield deep insights into the evolving public narrative on health interventions, informing future policymaking and healthcare strategies.
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