Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Oct 10, 2020
Date Accepted: May 10, 2022
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
DAO: An Ontology for Substance Use Epidemiology on Social Media and Dark Web
ABSTRACT
Background:
Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the utilization of these novel data sources for epidemiological surveillance of substance use behaviors and trends.
Objective:
The key aims are to describe the development and application of the Drug Abuse Ontology as a framework for analyzing web-based data to inform public health surveillance for the following applications: 1) determining user knowledge, attitudes, and behaviors related to non-medical use of buprenorphine and other illicit opioids through analysis of web forum data; 2) understanding patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the U.S through analysis of Twitter and web forum data; and 3) gleaning trends in the availability of novel synthetic opioids through analysis of crypto market data.
Methods:
The domain and scope of the drug abuse ontology were defined using competency questions from two popular ontology methodologies (Neon and 101 ontology development methodology). The quality of the ontology is evaluated with a set of tools and best practices recognized by the Semantic Web community and the AI community that engage in natural language processing. The standard ontology metrics are also presented.
Results:
The current version of Drug Abuse Ontology comprises 315 classes, 31 relationships, and 814 instances among the classes. The ontology is flexible and can easily accommodate new concepts. The integration of the ontology with machine learning algorithms dramatically decreases the false alarm rate by adding external knowledge to the learning process. The ontology is being updated to capture evolving concepts and has been used for four different projects: PREDOSE, eDrugTrends, eDarkTrends, DAO applications in Mental Health and COVID scenario.
Conclusions:
It has been found that the developed Drug Abuse Ontology (DAO) is useful to identify the most frequently used terms/slang terms on social media/dark web related to drug abuse posted by the general population .
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