Journal article
DUI: the drug use insights web server
Bioinformatics (Oxford, England), Vol.37(24), pp.4895-4897
12/11/2021
DOI: 10.1093/bioinformatics/btab461
PMID: 34164647
Abstract
Substance abuse constitutes one of the major contemporary health epidemics. Recently, the use of social media platforms has garnered interest as a novel source of data for drug addiction epidemiology. Often however, the language used in such forums comprises slang and jargon. Currently, there are no publicly available resources to automatically analyze the esoteric language-use in the social media drug-use sub-culture. This lacunae introduces critical challenges for interpreting, sensemaking and modeling of addiction epidemiology using social media.
Drug-Use Insights (DUI) is a public and open-source web application to address the aforementioned deficiency. DUI is underlined by a hierarchical taxonomy encompassing 108 different addiction related categories consisting of over 9000 terms, where each category encompasses a set of semantically related terms. These categories and terms were established by utilizing thematic analysis in conjunction with term embeddings generated from 7 472 545 Reddit posts made by 1 402 017 redditors. Given post(s) from social media forums such as Reddit and Twitter, DUI can be used foremost to identify constituent terms related to drug use. Furthermore, the DUI categories and integrated visualization tools can be leveraged for semantic- and exploratory analysis. To the best of our knowledge, DUI utilizes the largest number of substance use and recovery social media posts used in a study and represents the first significant online taxonomy of drug abuse terminology.
The DUI web server and source code are available at: http://haddock9.sfsu.edu/insight/.
Supplementary data are available at Bioinformatics online.
Details
- Title: Subtitle
- DUI: the drug use insights web server
- Creators
- Zachary Prince - San Francisco State UniversityDeeptanshu Jha - San Francisco State UniversityRahul Singh - San Francisco State University
- Resource Type
- Journal article
- Publication Details
- Bioinformatics (Oxford, England), Vol.37(24), pp.4895-4897
- DOI
- 10.1093/bioinformatics/btab461
- PMID
- 34164647
- ISSN
- 1367-4803
- eISSN
- 1367-4811
- Language
- English
- Date published
- 12/11/2021
- Academic Unit
- Computer Science
- Record Identifier
- 9984446269802771
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