Language, Social Support, and Recovery-Stage Transitions in Opioid Use Disorder on Reddit: Computational Analysis

Recovery from opioid use disorder (OUD) is a complex, nonlinear process involving substantial health, psychological, and social challenges. Although online social support has been shown to benefit individuals with OUD, less is known about how recovery stages, such as the initial or stable stages, are expressed and experienced in online communities. Specifically, the linguistic features characterizing each stage, the social support exchanged at each stage, and the feasibility of predicting these stage transitions from user-generated content remain largely unexplored.

This study aimed to develop a computational framework and present empirical insights for understanding OUD recovery in online communities by characterizing the language individuals use at different stages, the social support they receive, and the transitions they undergo over time.

We collected 32,810 posts and 324,224 comments from r/OpiatesRecovery, the largest Reddit community dedicated to opioid recovery from 2014 to 2022. We fine-tuned pretrained language models to classify posts into 5 recovery stages and identify 11 categories of social support in comments. Recovery trajectories were constructed for 2936 users who posted multiple times. Mann-Whitney U tests and one-way multivariate analysis of covariance were used to compare linguistic features and social support across recovery stages and transitions. In addition, we predicted recovery-stage transitions using fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) and open-source large language models (Llama-3.1-8B-Instruct and Qwen2.5-14B-Instruct) evaluated in zero-shot and few-shot settings.

Individuals in early-stage recovery used significantly more negative, painful, and passive language compared to those in later stages (P<.001). They also received more informational support (eg, advice and factual guidance) but less emotional support (eg, encouragement and sympathy; P<.001). Notably, posts followed by observed recovery-stage progression were associated with significantly more informational support than posts followed by no observed stage change (P<.001). Regarding the prediction of individuals' future recovery transitions, fine-tuned RoBERTa outperformed prompted open-source large language model baselines in this benchmark (F₁-score=0.59 vs 0.43), although this task remained highly challenging.

This study reveals distinct linguistic and social support patterns across OUD recovery stages, identifying an association between informational support and observed recovery-stage progression. Although automatically detecting stage transitions remains challenging, these findings may inform future research on timely, stage-appropriate support strategies in online recovery communities.