Evidence map›Paper›PMID 39655044›Full record

ArticleDrug and alcohol dependence reports2024

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

E Holbrook, B Wiskur, Z Nagykaldi

Abstract read
In one paragraph

Article in Drug and alcohol dependence reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Interpol review of forensic drug chemistry, 2022-2025.Forensic science international. Synergy · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

E HolbrookDepartment of Family Medicine, OU College of Medicine, University of Oklahoma Health Sciences Center, Japan.
B WiskurDepartment of Family Medicine, OU College of Medicine, University of Oklahoma Health Sciences Center, Japan.
Z NagykaldiDepartment of Family Medicine, OU College of Medicine, University of Oklahoma Health Sciences Center, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Machine learningOpioid substance useOpioid use disorderRedditWord2Vec

Identifiers

PMID39655044
PMCPMC11626628

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.