Detecting risk level in individuals misusing fentanyl utilizing posts from an online community on Reddit

Area of Science:

• Computational social science
• Public health informatics
• Machine learning applications in substance abuse research

Background:

• Opioid misuse, particularly of synthetic opioids like fentanyl, is a critical US public health crisis.
• Identifying high-risk individuals misusing fentanyl is challenging due to their hidden nature.
• Novel methods are needed to detect individuals at risk for fentanyl misuse.

Purpose of the Study:

• To leverage machine learning to identify risky content within online discussions of fentanyl on Reddit.
• To develop and validate machine learning classification models for fentanyl misuse risk detection.
• To create a vocabulary of colloquial terms used for fentanyl and its analogues.

Main Methods:

• Development of a 12-category codebook by clinical experts to define fentanyl misuse risk.
• Manual labeling of 391 Reddit posts and comments based on the developed codebook.
• Training and evaluation of machine learning classification models using the labeled data.

Main Results:

• The machine learning risk model achieved 76% accuracy and 76% sensitivity in detecting risky posts/comments.
• A comprehensive vocabulary of community-specific, colloquial terms for fentanyl and its analogues was generated.
• The study demonstrated the feasibility of using AI to analyze online discourse for substance misuse risk.

Conclusions:

• An interdisciplinary approach combining machine learning and clinical expertise can automatically detect risky online discourse for timely intervention.
• The generated vocabulary enhances understanding of online "street" nomenclature for opioids.
• Findings facilitate the identification of risk factors and inform tailored outreach and intervention strategies for individuals misusing fentanyl.