Reasoning, Contrastive, and In-Context Strategies for Opioid Use Stage Detection on Social Media

Abstract

The opioid epidemic has ravaged the US for the past two decades and is still a persistent threat. During the same time, the increasing use of social media has created a new avenue for people to share their journeys regarding opioid use. In this context, research in automatically determining opioid use stages (e.g., misuse, addiction, recovery) based on self disclosures in social media posts is gaining traction. In this paper, using a recent benchmark, we assess different supervised strategies for identifying self-disclosed opioid use stages from Reddit posts. We consider distilled reasoning traces from DeepSeek R1 (an open weights reasoning model), supervised contrastive learning (SCL), and few-shot in-context learning (ICL) with GPT-5 to conduct a variety of experiments with encoder and encoder-decoder models. We also conduct direct zero-shot (ZS) experiments with GPT 5 and GPT 5.2. Across different models and datasets, our strategies provide improvements in performance with some nuances that are too subtle to elaborate in the abstract. A surprising finding is that ZS results with GPT-5 are better than all supervised results, which ushers a new frontier for LLM-based classification of opioid use in social media posts. Our code is available for reuse and replication: https://github.com/bionlproc/Opioid-Stage.

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