Self-Disclosure and Social Support in a Web-Based Opioid Recovery Community: Machine Learning Analysis

Area of Science:

• Public Health
• Computer Science
• Sociology

Background:

• Opioid use disorder (OUD) presents a significant public health challenge.
• Online communities offer anonymous and accessible platforms for individuals in recovery.
• Limited research exists on self-disclosure and social support within these digital recovery spaces.

Purpose of the Study:

• To analyze the content users disclose in the r/OpiatesRecovery Reddit community.
• To identify the types of social support users receive.
• To examine the relationship between disclosed content and received social support.

Main Methods:

• Analysis of 32,810 posts and 324,224 comments from r/OpiatesRecovery (2014-2022).
• Mixed-methods approach using coding for recovery stages, self-disclosure, and goals.
• Machine learning for analysis scaling and regression to link content with support.

Main Results:

• Informational self-disclosure (88.3%) and seeking informational support (58.4%) were prevalent.
• Posts with informational self-disclosure received more advice and facts.
• Emotional self-disclosure predicted higher emotional support and personal experiences.
• Individuals in early recovery stages received more advice but less emotional support.

Conclusions:

• Self-disclosure is crucial for fostering social support in online OUD recovery communities.
• There is a need to enhance engagement from individuals in stable recovery.
• Online support groups can serve as valuable complements to conventional recovery interventions.