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

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 var...

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Vydáno v:Drug and alcohol dependence reports Ročník 13; s. 100302
Hlavní autoři: Holbrook, E., Wiskur, B., Nagykaldi, Z.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Netherlands Elsevier B.V 01.12.2024
Elsevier
Témata:
Full Length Report
Machine learning
Opioid substance use
Opioid use disorder
Psychiatric/Mental Health
Reddit
Word2Vec
Opioid substance use
Opioid use disorder
Reddit
Word2Vec
Machine learning
ISSN:2772-7246, 2772-7246
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Shrnutí: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. 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. 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. 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. •Over 220,000 new mentions of opioid-related terms identified, representing a 200 % increase.•Utilized Gensim and Word2Vec to develop an auto-encoding neural network for slang detection.•Leveraged Reddit discussions to uncover timely and nuanced drug-related slang.•Found strong semantic relationships between opioids and stimulants, depressants, and hallucinogens.•Developed a fast and efficient method for detecting slang, reducing manual effort.
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ISSN:2772-7246
2772-7246
DOI:10.1016/j.dadr.2024.100302