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Currently submitted to: JMIR Formative Research

Date Submitted: Jan 12, 2025
Open Peer Review Period: Jan 12, 2025 - Mar 9, 2025
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Self-Disclosure and Social Support in an Online Opioid Recovery Community: A Machine Learning Analysis

  • Yu Chi; 
  • Huai-yu Chen; 
  • Khushboo Thaker

ABSTRACT

Background:

The opioid crisis remains a critical public health challenge, with opioid use disorder (OUD) imposing significant societal and healthcare burdens. Online communities, such as the Reddit community r/OpiatesRecovery, provide an anonymous and accessible platform for individuals in recovery. Despite the increasing use of Reddit for substance use research, limited studies have explored the content and interactions of self-disclosure and social support within these communities.

Objective:

This study aims to address the following research questions: (1) What content do users disclose in the community? (2) What types of social support do users receive? (3) How does the content disclosed relate to the type and extent of social support received?

Methods:

We analyzed 32,810 posts and 324,224 comments from r/OpiatesRecovery spanning eight years (2014–2022) using a mixed-method approach. Posts were coded for recovery stages, self-disclosure, and goals, while comments were categorized into informational and emotional support types. A machine learning-based classifier was employed to scale the analysis. Regression analyses were conducted to examine the relationship between post content and received support.

Results:

The majority of posts were made by individuals using opioids (22.0%) or in initial recovery stages (less than 1 month of abstinence; 27.7%). However, posts by individuals in stable recovery (abstinence for more than five years) accounted for only 1.8%. Informational self-disclosure appeared in 88.3% of posts, while emotional self-disclosure was present in 75.6%. Posts seeking informational support (58.4%) were far more common than those seeking emotional support (2.4%). On average, each post received 9.88 comments (SD = 11.36). The most frequent types of support were fact and situational appraisal (M = 5.62, SD = 6.82) and personal experience (M = 4.88, SD = 5.98), while referral was least common (M = 0.61, SD = 0.50). Regression analyses revealed significant relationships between self-disclosure and received support. Posts containing informational self-disclosure were more likely to receive advice (β = 0.17, p < .001), facts (β = 0.30, p < .001), and opinions (β = 0.11, p < .001). Emotional self-disclosure predicted higher levels of emotional support (β = 0.17, p < .001) and personal experiences (β = 0.07, p < .001). Posts from individuals in addiction stage received more advice (β = -0.06, p < .001) but less emotional support (β = -0.05, p < .001) compared to posts from individuals in later recovery stages.

Conclusions:

This study highlights the role of self-disclosure in fostering social support within online OUD recovery communities. Findings suggest a need for increasing engagement from individuals in stable recovery stages and improving the diversity and quality of social support. By uncovering interaction patterns, this study provides valuable insights for leveraging online platforms as complementary resources to traditional recovery interventions.


 Citation

Please cite as:

Chi Y, Chen Hy, Thaker K

Self-Disclosure and Social Support in an Online Opioid Recovery Community: A Machine Learning Analysis

JMIR Preprints. 12/01/2025:71207

DOI: 10.2196/preprints.71207

URL: https://preprints.jmir.org/preprint/71207

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