TY - JOUR
T1 - Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research
T2 - Ontology Development Study
AU - Lokala, Usha
AU - Lamy, Francois
AU - Daniulaityte, Raminta
AU - Gaur, Manas
AU - Gyrard, Amelie
AU - Thirunarayan, Krishnaprasad
AU - Kursuncu, Ugur
AU - Sheth, Amit
N1 - Funding Information:
This work was supported in part by the National Institute on Drug Abuse (NIDA) grant 5R01DA039454-02 Trending: Social Media Analysis to Monitor Cannabis and Synthetic Cannabinoid Use; the National Science Foundation award 1761931 Spokes: MEDIUM: MIDWEST: Collaborative: Community-Driven Data Engineering for Substance Abuse Prevention in the Rural Midwest; the NIDA grant 5R21DA044518-02 eDarkTrends: Monitoring Darknet Markets to Track Illicit Synthetic Opioid Trends; and the National Institutes of Health grant R21 DA030571-01A1 A Study of Social Web Data on Buprenorphine Abuse using Semantic Web Technology. Any opinions, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the National Science Foundation, National Institutes of Health, or NIDA.
Funding Information:
This is our second project that received funding from NIH and National Institute on Drug Abuse (NIDA) in 2014 [45]. This study focused on social media data related to cannabis and synthetic cannabinoid use in the context of evolving cannabis legalization policies in the United States. The aim of this study was to develop eDrugTrends, a comprehensive software platform for semiautomated processing and visualization of thematic, sentiment, spatiotemporal, and social network dimensions of social media data (Twitter and web forums) on cannabis and synthetic cannabinoid use. The study also aimed to (1) identify and compare trends in knowledge, attitudes, and behaviors related to cannabis and synthetic cannabinoid use across United States regions with different cannabis legalization policies using Twitter and web forum data and (2) analyze social network characteristics and identify key influencers in cannabis and synthetic cannabinoid–related discussions on Twitter. For addressing these aims of the eDrugTrends platform, the DAO was expanded further to include a more comprehensive representation of emerging cannabis products, synthetic cannabinoid products, health-related consequences, and mental health conditions.
Publisher Copyright:
© Usha Lokala, Francois Lamy, Raminta Daniulaityte, Manas Gaur, Amelie Gyrard, Krishnaprasad Thirunarayan, Ugur Kursuncu, Amit Sheth.
PY - 2022/12
Y1 - 2022/12
N2 - 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 use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. Objective: The key aims were 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: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America 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 determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing. Results: The current version of the 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: The 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.
AB - 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 use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. Objective: The key aims were 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: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America 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 determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing. Results: The current version of the 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: The 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.
KW - cryptomarket
KW - illicit drugs
KW - knowledge graph
KW - ontology
KW - semantic web
KW - social media
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UR - http://www.scopus.com/inward/citedby.url?scp=85144637240&partnerID=8YFLogxK
U2 - 10.2196/24938
DO - 10.2196/24938
M3 - Article
C2 - 36563032
AN - SCOPUS:85144637240
VL - 8
JO - JMIR Public Health and Surveillance
JF - JMIR Public Health and Surveillance
SN - 2369-2960
IS - 12
M1 - e24938
ER -