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

Sanjana Garg, Jordan Taylor, Mai El Sherief, Erin Kasson, Talayeh Aledavood, Raven Riordan, Nina Kaiser, Patricia Cavazos-Rehg, Munmun De Choudhury*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)
111 Downloads (Pure)


Introduction: Opioid misuse is a public health crisis in the US, and misuse of synthetic opioids such as fentanyl have driven the most recent waves of opioid-related deaths. Because those who misuse fentanyl are often a hidden and high-risk group, innovative methods for identifying individuals at risk for fentanyl misuse are needed. Machine learning has been used in the past to investigate discussions surrounding substance use on Reddit, and this study leverages similar techniques to identify risky content from discussions of fentanyl on this platform. Methods: A codebook was developed by clinical domain experts with 12 categories indicative of fentanyl misuse risk, and this was used to manually label 391 Reddit posts and comments. Using this data, we built machine learning classification models to identify fentanyl risk. Results: Our machine learning risk model was able to detect posts or comments labeled as risky by our clinical experts with 76% accuracy and 76% sensitivity. Furthermore, we provide a vocabulary of community-specific, colloquial words for fentanyl and its analogues. Discussion: This study uses an interdisciplinary approach leveraging machine learning techniques and clinical domain expertise to automatically detect risky discourse, which may elicit and benefit from timely intervention. Moreover, our vocabulary of online terms for fentanyl and its analogues expands our understanding of online “street” nomenclature for opiates. Through an improved understanding of substance misuse risk factors, these findings allow for identification of risk concepts among those misusing fentanyl to inform outreach and intervention strategies tailored to this at-risk group.

Original languageEnglish
Article number100467
Number of pages10
JournalInternet Interventions
Publication statusPublished - Dec 2021
MoE publication typeA1 Journal article-refereed


  • Detection
  • Fentanyl
  • Machine learning
  • Opioids
  • Overdose
  • Social media


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