The use of artificial intelligence methods in Reddit to investigate opioid use: a scoping review
Notice bibliographique
Résumé
Opioid use disorder (OUD) is a chronic condition that affects more than 40 million people worldwide1. In 2019, the Global Burden of Disease study estimated 494,000 deaths and 30.9 million years of "healthy" life lost because of premature death and disability attributable to the use of drugs. Twenty-six percent (128,000) of these deaths were attributed to drug use disorders, of which OUD contributed to 69%. Most of the disability-adjusted life years (DALYs) (71%) attributed to drug use disorders were caused by OUD, corresponding to 12.9 million DALYs2. The United States (U.S.) accounts for a large share of these deaths, with over 107,000 deaths directly attributable to overdose in 2021, of which 75% were related to opioid use3. Among the 2.5 million people aged 12 or older with a past year of OUD in the U.S., only 11.2% (or 278,000 people) received medications for OUD (MOUD) in the past year4 and among people with OUD (PWOUD) receiving MOUD, drug use and relapse are the leading cause of death5–8. Understanding experiences, concerns, challenges, and sources of support among people who use opioids is the first step to designing interventions tailored to their needs. Social media platforms represent an important and accessible source of community support for people who use opioids, given they facilitate getting technical "know-"ow" and support from peers. These platforms often allow for anonymous participation, leading to authentic accounts of both positive and negative experiences with drugs (including in the context of treatment) and daily life situations that may impact their physical and mental health and that may be addressed through appropriate interventions. Reddit, a community-based social media platform where people participate in discussions anonymously, has been studied as a rich virtual place gathering people willing to share their experiences with opioids and related issues. This platform is one of the most popular online platforms of interaction and has provided a space for exchange and discussion since 2005, mainly used by English speakers9. Given the high rate of OUD in the U.S. and Canada, Australia, and the United Kingdom, it represents a valuable source of data to better understand the daily experiences of people who use opioids in these settings. Analyzing such large amounts of data is challenging. Beyond the traditional qualitative approach to analyzing textual data, artificial intelligence methods such as natural language processing, sentiment analysis, and supervised and unsupervised machine learning techniques have been used to facilitate content extraction, unveil discussion topics, and learn and predict "behaviors". These techniques enable the use of a massive textual corpus and the exploration of a multitude of research questions based on the perspectives of people sharing their experiences on social media platforms such as Reddit. This study aims to rigorously document how artificial intelligence methods have been applied to Reddit forums to study opioid use. More specifically, we are interested in mapping the main questions asked in these studies, their overarching goals and key objectives, the methodologies and software used, and their main limitations. We also aim to systematically collect information on a dictionary or key terms used in these studies to synthesize a comprehensive dictionary of embedding words allowing for the broader research community to rigorously investigate opioid use in Reddit using textual pre-processing algorithms.
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 | 0,019 | 0,075 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,007 |
| Bibliométrie | 0,025 | 0,020 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».