Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Oct 10, 2020
Date Accepted: May 10, 2022
Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study
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 (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: 1) determining user knowledge, attitudes, and behaviors related to non-medical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data (PREDOSE); 2) analyzing 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 (eDrugTrends), and 3) assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); 4) COVID-19 trends in social media data related to 13 states in USA as per Mental Health America (MHA) reports.
Methods:
The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The 101 method includes 1. determining the domain and scope of ontology; 2. Reusing existing knowledge; 3. Enumeration of important terms in ontology; 4. Defining the classes and its properties and creating instances of the class. The quality of the ontology was 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.
Results:
The current version of DAO 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 decreased the false alarm rate by adding external knowledge to the machine learning process. The ontology is recurrently updated to capture evolving concepts in different contexts and applied to analyze data related to social media and dark web marketplaces.
Conclusions:
DAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research.
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