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.

Opioid abuse and addiction remain a major public health challenge in the US. At a broad level, barriers to recovery often take the form of individual, social, and structural issues. However, it is crucial to know the specific barriers patients face to help design better treatment interventions and healthcare policies. Researchers typically discover barriers through focus groups and surveys. While scientists can exercise better control over these strategies, such methods are both expensive and time consuming, needing repeated studies across time as new barriers emerge. We believe, this traditional approach can be complemented by automatically mining social media to determine high-level trends in both well-known and emerging barriers. In this paper, we report on such an effort by mining messages from the r/OpiatesRecovery subreddit to extract, classify, and examine barriers to opioid recovery, with special attention to the COVID-19 pandemic’s impact. Our methods involve multi-stage prompting to arrive at barriers from each post and map them to existing barriers or identify new ones. The new barriers are refined into coherent categories using embedding-based similarity measures and hierarchical clustering. Temporal analysis shows that some stigma-related barriers declined (relative to pre-pandemic), whereas systemic obstacles—such as treatment discontinuity and exclusionary practices—rose significantly during the pandemic. Our method is general enough to be applied to barrier extraction for other substance abuse scenarios (e.g., alcohol or stimulants)

We describe the methods and results of our submission to the 9th Social Media Mining for Health Research and Applications (SMM4H) 2024 shared tasks 4 and 5. Task 4 involved extracting the clinical and social impacts of non-medical substance use and task 5 focused on the binary classification of tweets reporting children’s medical disorders. We employed encoder language models and their ensembles, achieving the top score on task 4 and a high score for task 5.