JMIR Publications

ABSTRACT

Background:

Recovery from opioid use disorder (OUD) is a complex, non-linear 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.

Objective:

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.

Methods:

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 five recovery stages and identify 11 categories of social support in comments. Recovery trajectories were constructed for 2,936 users who posted multiple times. Mann-Whitney U tests and one-way multivariate analysis of covariance (MANCOVA) were used to compare linguistic features and social support across recovery stages and transitions. In addition, we predicted recovery stage transition using fine-tuned RoBERTa and zero-shot ChatGPT models.

Results:

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 (e.g., advice and factual guidance) but less emotional support (e.g., encouragement and sympathy) (P <.001). Notably, users who progressed from an earlier to a later stage of recovery received significantly more informational support compared to those whose recovery stage remained unchanged (P <.001). Regarding the prediction of individuals’ future recovery transitions, fine-tuned pretrained language models outperformed zero-shot ChatGPT models (F1: 0.57 vs. 0.42), though this task remained highly challenging.

Conclusions:

This study reveals distinct linguistic and social support patterns across OUD recovery stages, identifying a critical link between informational support and successful stage progression. These patterns suggest opportunities for developing stage-appropriate interventions in online recovery communities. While automatically detecting stage transitions remains challenging, the framework in this study offers promising potential for delivering timely support