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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Drug and Alcohol Dependence Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772724624000866 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850061245205446656 |
|---|---|
| author | E. Holbrook B. Wiskur Z. Nagykaldi |
| author_facet | E. Holbrook B. Wiskur Z. Nagykaldi |
| author_sort | E. Holbrook |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6c329de0c25444b595aede6484a31bcd |
| institution | DOAJ |
| issn | 2772-7246 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Drug and Alcohol Dependence Reports |
| spelling | doaj-art-6c329de0c25444b595aede6484a31bcd2025-08-20T02:50:19ZengElsevierDrug and Alcohol Dependence Reports2772-72462024-12-011310030210.1016/j.dadr.2024.100302Discovering opioid slang on social media: A Word2Vec approach with reddit dataE. Holbrook0B. Wiskur1Z. Nagykaldi2Department of Family Medicine, OU College of Medicine, University of Oklahoma Health Sciences Center, JapanCorresponding author.; Department of Family Medicine, OU College of Medicine, University of Oklahoma Health Sciences Center, JapanDepartment of Family Medicine, OU College of Medicine, University of Oklahoma Health Sciences Center, JapanThe 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.http://www.sciencedirect.com/science/article/pii/S2772724624000866Opioid use disorderOpioid substance useRedditWord2VecMachine learning |
| spellingShingle | E. Holbrook B. Wiskur Z. Nagykaldi Discovering opioid slang on social media: A Word2Vec approach with reddit data Drug and Alcohol Dependence Reports Opioid use disorder Opioid substance use Word2Vec Machine learning |
| title | Discovering opioid slang on social media: A Word2Vec approach with reddit data |
| title_full | Discovering opioid slang on social media: A Word2Vec approach with reddit data |
| title_fullStr | Discovering opioid slang on social media: A Word2Vec approach with reddit data |
| title_full_unstemmed | Discovering opioid slang on social media: A Word2Vec approach with reddit data |
| title_short | Discovering opioid slang on social media: A Word2Vec approach with reddit data |
| title_sort | discovering opioid slang on social media a word2vec approach with reddit data |
| topic | Opioid use disorder Opioid substance use Word2Vec Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772724624000866 |
| work_keys_str_mv | AT eholbrook discoveringopioidslangonsocialmediaaword2vecapproachwithredditdata AT bwiskur discoveringopioidslangonsocialmediaaword2vecapproachwithredditdata AT znagykaldi discoveringopioidslangonsocialmediaaword2vecapproachwithredditdata |