Evidence map›Paper›PMID 40674691›Full record

ArticleJMIR formative research2025

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

Yu Chi, Huai-Yu Chen, Khushboo Thaker

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yu ChiSchool of Information, College of Information, Data and Society, San Jose State University, 1 Washington Sq, San Jose, CA, 95192, United States, 1 4125396621.ORCID 0000-0001-8838-3023
Huai-Yu ChenDepartment of Communication, College of Communication and Information, University of Kentucky, Lexington, KY, United States.ORCID 0000-0002-6150-2501
Khushboo ThakerSchool of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States.ORCID 0000-0003-3619-9376

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The opioid crisis remains a critical public health challenge, with opioid use disorder (OUD) imposing significant societal and health care burdens. Web-based 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?; and (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 8 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 used 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 (7225/32,810, 22.0%) or in initial recovery stages (less than 1 mo of abstinence; 27.7%). However, posts by individuals in stable recovery (abstinence for more than 5 years) accounted for only 1.8%. Informational self-disclosure appeared in 88.3% (n=28,977) of posts, while emotional self-disclosure was present in 75.6% (n=24,816). Posts seeking informational support (19,153/32,810, 58.4%) were far more common than those seeking emotional support (779/32,810, 2.4%). On average, each post received 9.88 (SD 11.36) comments. The most frequent types of support were fact and situational appraisal (mean 5.62, SD 6.82) and personal experience (mean 4.88, SD 5.98), while referral was the least common (mean 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 the addiction stage received more advice (β=-0.06, P<.001) but less emotional support (β=-0.05, P<.001) compared with posts from individuals in later recovery stages. Conclusions: This study highlights the role of self-disclosure in fostering social support within web-based 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 support groups as complementary resources to traditional recovery interventions.

Indexed as

InternetMachine LearningOpioid-Related DisordersSelf DisclosureSocial SupportAdultFemaleHumansMaleSocial Medianatural language processingonline health communityopioidpeer supportrecoveryRedditsocial mediasubstance use

Identifiers

PMID40674691
PMCPMC12289226

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.