Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study.

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.

The effect of COVID on cryptomarkets: The impact of the pandemic on the level of success in drug transactions

In the months following the onset of the COVID-19 pandemic, the European Monitoring Center for Drugs and Drug Addiction (2020) observed an increased use of cryptomarkets, leading researchers to question whether cryptomarkets constitute a more practical channel for the distribution of illicit drugs since they make it possible to avoid physical contact. However, measures to counter the spread of the virus also led to border closures and it is possible that cryptomarkets were negatively affected by this change. This study measures and evaluates the success rate of cryptomarket transactions during the COVID-19 pandemic, using self-reported data on the outcome of cryptomarket transactions. To collect self-reported data on cryptomarket transactions, we launched a platform that allowed participants to provide information about past cryptomarket activities. The sample consists of 591 valid self-reports received between January 1, 2020, and August 21, 2020. Logistic regressions on cryptomarket data before and after the onset of the pandemic allowed us to observe any changes. We found that the increase in the number of failed transactions parallelled the global spread of the pandemic, while the international and intercontinental nature of the transactions was associated with delivery failure. It is probable that cryptomarkets for drugs have been disrupted due to the pandemic. © Les Presses de l'Université de Montréal, 2021

The success rate of online illicit drug transactions during a global pandemic.

BACKGROUND AND AIMS: In the months following the onset of the COVID-19 pandemic, the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA; 2020) observed an increased use of cryptomarkets, which led them to question whether cryptomarkets constituted a more convenient channel via which to distribute illicit drugs without any in-person contact. However, as more countries' borders closed, the likelihood is that cryptomarkets have been negatively impacted. We aim to measure and understand the success rate of transactions on cryptomarkets during the ongoing COVID-19 pandemic, through recourse to self-reported data that documents the outcome of cryptomarket transactions. METHODS: To collect self-reported data on cryptomarket transactions, we launched a platform where participants can enter information about their prior activities on cryptomarkets. The sample consists of 591 valid self-reports that were received between January 1st, 2020 and August 21st, 2020. RESULTS: The number of unsuccessful transactions increased concurrently with the global spread of the pandemic. Both the international and inter-continental nature of the transactions and the severity of the crisis in the vendor's country are significantly associated with delivery failure. CONCLUSIONS: Drug cryptomarkets may have been disrupted due to the pandemic. The results lead to two opposing explanations for unsuccessful transactions. One explanation for the lower success rate is the inability of drug dealers to deliver on past promises that were made in good faith, while the second points towards opportunistic and abusive behaviour by drug dealers.