Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Oct 26, 2020)
Date Submitted: Jul 27, 2020
Warning: This is an author submission that is not peer-reviewed or edited.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.
Development and Application of Drug Abuse Ontology for the Analysis of substances of abuse 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 development and application of the Drug Abuse Ontology as a framework for analyzing web-based data to inform public health surveillance in the following domains: 1) user knowledge, attitudes, and behaviors related to non-medical use of buprenorphine and other illicit opioids through analysis of web forum data; 2) 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) 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. The ontology was manually developed by the domain experts from the Center for Interventions, Treatment, and Addictions Research (CITAR) who used a range of data sources: 1) key epidemiological data sources and reports accessible through National Institute on Drug Abuse, Drug Enforcement Agency, European Monitoring Centre for Drugs Addiction, RxNorm and other; 2) prior peer-reviewed publications related to illicit opioids, cannabis, and other drugs; and 3) preliminary assessment and examination of web-based, social media sources related to selected substances. Sources of types 1 and 2 provided primary concepts while sources of type 3 were important in identifying alternative concepts including synonyms and street names.
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 three different projects: PREDOSE, eDrugTrends, and eDarkTrends.
Conclusions:
It is found that the developed 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 on social media and vendors on the dark web.
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