Discovering opioid slang on social media: A Word2Vec approach with reddit data

Abstract

The CDC reported that the overdose of prescription or illicit opioids was responsible for the deaths of over 80,000 Americans in 2021. Social media is a valuable source of insight into problematic patterns of substance misuse. The way people converse with illicit drugs in online forums is highly variable, and slang terms are frequently used. Manually identifying names of specific drugs can be difficult in both time and labor. Subjects and methods: The study utilized the Gensim Python library and its Word2Vec neural network model to develop an auto-encoding neural network, enabling the innovative analysis of drug-related discourse downloaded from the Reddit website. The slang terms were then used to qualitatively analyze the topics and categories of drugs discussed on the forum. Results: The inclusion of slang terms facilitated the introduction of 200,000 specific mentions of opioid drugs and that stimulant drugs share a substantial semantic similarity with opioids, a 200 % increase in the number of drug-related terms as compared to using existing datasets. Conclusions: This study advances the academic field with an extended collection of drug-related terms, offering a useful methodology and resource for tackling the opioid crisis with innovative, reduced-time detection and surveillance methods.

Keywords

• Opioid use disorder
• Opioid substance use
• Reddit
• Word2Vec
• Machine learning