Self-Disclosure and Social Support in a Web-Based Opioid Recovery Community: Machine Learning Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».