The relationship between cryptomarket drug purchase, social networks and adverse drug events: A cross-sectional study.

INTRODUCTION: Drug use and trading are typically social activities; however, supply through cryptomarkets can occur without any in-person social contact. People who use drugs alone may be at higher risk of experiencing harms, for example, due to lack of others who may call for emergency assistance. Alternatively, cryptomarkets may be a source of harm reduction information and drugs with better-known content and dose, potentially reducing the risk of adverse events. This study examines relationships between cryptomarket use, drug-using social networks and adverse drug events for MDMA, cocaine and LSD. METHOD: A subsample of 23,053 respondents from over 70 countries was collected in the 2018 Global Drug Survey. People who reported using MDMA, cocaine or LSD were asked about using cryptomarkets to purchase these drugs; any adverse drug events requiring medical treatment (combining seeking treatment and should have sought treatment but did not); and social networks who they had used the specific drug with. All measures referred to the last 12 months, hereon referred to as 'recent'. Binary logistic regressions examined relationships between cryptomarket use, drug-using social networks, and adverse drug events, controlling for age, gender, and frequency of drug use. RESULTS: Adverse events from any drug type were low (5.2%) and for each drug; MDMA (3.5%); cocaine (3.3%); and LSD (3.5%). After controlling for covariates, recent cryptomarket use was associated with increased likelihood of having no drug-using network for each drug type. People who recently used cryptomarkets were more likely to report adverse cocaine (AOR = 1.70 (1.22-2.37)) and LSD (AOR = 1.58 (1.12-2.09)) events. For those reporting a network size >1, network characteristics did not differ with recent cryptomarket use; however, those reporting recent cryptomarket use were more likely to report adverse LSD events (AOR = 1.86 (0.99-3.51)). CONCLUSION: People who reported purchasing drugs from cryptomarkets more commonly reported having no drug-using network, and cryptomarket purchase was associated with reported adverse events. Our results support the notion that cryptomarket use increases drug-related harm, but further disentanglement of multiple complex mechanisms is needed in future research.

Uncertainty and risk: A framework for understanding pricing in online drug markets.

BACKGROUND: The pricing of illicit drugs is typically approached within the risks and prices framework. Recent sociological and economic studies of prices in online drug markets have stressed the centrality of reputation for price formation. In this paper, we propose an account of price formation that is based on the risks and prices framework, but also incorporates internal social organization to explain price variation. We assess the model empirically, and extend the current empirical literature by including payment methods and informal ranking as influences on drug pricing. METHODS: We apply our model to estimate the prices of cannabis, cocaine, and heroin in two online drug markets, cryptomarkets (n = 92.246). Using multilevel linear regression, we assess the influence of product qualities, reputation, payment methods, and informal ranking on price formation. RESULTS: We observe extensive quantity discounts varying across substances and countries, and find premia and discounts associated with product qualities. We find evidence of payment method price adjustment, but contrary to expectation we observe conflicting evidence concerning reputation and status. We assess the robustness of our findings concerning reputation by comparing our model to previous approaches and alternative specifications. CONCLUSION: We contribute to an emerging economic sociological approach to the study illicit markets by developing an account of price formation that incorporates cybercrime scholarship and the risks and prices framework. We find that prices in online drug markets reflect both external institutional constraint and internal social processes that reduce uncertainty.

Illicit drug prices and quantity discounts: A comparison between a cryptomarket, social media, and police data.

BACKGROUND: Illicit drugs are increasingly sold on cryptomarkets and on social media. Buyers and sellers perceive these online transactions as less risky than conventional street-level exchanges. Following the Risks & Prices framework, law enforcement is the largest cost component of illicit drug distribution. We examine whether prices on cryptomarkets are lower than prices on social media and prices reported by law enforcement on primarily offline markets. METHODS: Data consists of online advertisements for illicit drugs in Sweden in 2018, scraped from the cryptomarket Flugsvamp 2.0 (n = 826) and collected with digital ethnography on Facebook (n = 446). Observations are advertisements for herbal cannabis (n = 421), cannabis resin, hash (n = 594), and cocaine (n = 257) from 156 sellers. Prices are compared with estimates from Swedish police districts (n = 53). Three multilevel linear regression models are estimated, one for each drug type, comparing price levels and discount elasticities for each platform and between sellers on each platform. RESULTS: Price levels are similar on the two online platforms, but cocaine is slightly more expensive on social media. There are quantity discounts for all three drug types on both platforms with coefficients between -0.10 and -0.21. Despite the higher competition between sellers on cryptomarkets, prices are not lower compared to social media. Online price levels for hash and cocaine are similar to those reported by police at the 1 g level. CONCLUSION: Mean prices and quantity discounts are similar in the two online markets. This provides support for the notion that research on cryptomarkets can also inform drug market analysis in a broader sense. Online advertisements for drugs constitute a new detailed transaction-level data source for supply-side price information for research.

Fentanyl analogs on the Swedish webforum flashback: Interest and impact of scheduling.

BACKGROUND: Sweden regulates new psychoactive substances, including fentanyl analogs, individually. This reactive scheduling procedure enabled the existence of a recreational market for unscheduled fentanyl analogs sold from surface webshops. We measure the interest in 24 named fentanyl analogs and the impact of scheduling. METHODS: We scraped posts in threads on named fentanyl analogs from the Swedish internet forum Flashback.org, 2012-2019. The sample consists of 24 threads with a total of 8761 posts. We construct five measures of interest based on duration of threads, number of posts, and number of distinct posters, and fit a non-seasonal ARMA model to test if there was a change in mean activity after scheduling. RESULTS: Across the five measures, there was most interest in acryl fentanyl, butyr fentanyl, and acetyl fentanyl. The number of daily posts was significantly reduced in nine out of 13 threads after scheduling. CONCLUSION: The scheduling of fentanyl analogs impacted interest on Flashback.org. The biggest effect sizes were from the narcotics scheduling of 2-Me-MAF, acryl, and acetyl fentanyl, while furanyl fentanyl saw the biggest reduction after health scheduling. The reductions were bigger for narcotics scheduling compared to health scheduling.

Mixing politics and crime - The prevalence and decline of political discourse on the cryptomarket.

BACKGROUND: Dread Pirate Roberts, founder of the first cryptomarket for illicit drugs named Silk Road, articulated libertarian political motives for his ventures. Previous research argues that there is a significant political component present or involved in cryptomarket drug dealing which is specifically libertarian. The aim of the paper is to investigate the prevalence of political discourses within discussions of cryptomarket drug dealing, and further to research the potential changes of these over the timespan of the study. METHODS: We develop a novel operationalization of discourse analytic concepts which we combine with topic modelling enabling us to study how politics are articulated on cryptomarket forums. We apply the Structural Topic Model on a corpus extracted from crawls of cryptomarket forums encompassing posts dating from 2011 to 2015. RESULTS: The topics discussed on cryptomarket forums are primarily centered around the distribution of drugs including discussions of shipping and receiving, product advertisements, and reviews as well as aspects of drug consumption such as testing and consumption. However, on forums whose primary function is aiding operations on a black market, we still observe political matter. We identified one topic which expresses a libertarian discourse that emphasizes the individual's right to non-interference. Over time we observe an increasing prevalence of the libertarian discourse from 2011 to the end of 2013. In the end of 2013 - when Silk Road was seized - we observe an abrupt change in the prevalence of the libertarian discourse. CONCLUSIONS: The libertarian political discourse has historically been prevalent on cryptomarket forums. The closure of Silk Road has affected the prevalence of libertarian discourse suggesting that while the closure did not succeed in curtailing the cryptomarket economy, it dampened political sentiments.

A replication and methodological critique of the study "Evaluating drug trafficking on the Tor Network".

The development of cryptomarkets has gained increasing attention from academics, including growing scientific literature on the distribution of illegal goods using cryptomarkets. Dolliver's 2015 article "Evaluating drug trafficking on the Tor Network: Silk Road 2, the Sequel" addresses this theme by evaluating drug trafficking on one of the most well-known cryptomarkets, Silk Road 2.0. The research on cryptomarkets in general-particularly in Dolliver's article-poses a number of new questions for methodologies. This commentary is structured around a replication of Dolliver's original study. The replication study is not based on Dolliver's original dataset, but on a second dataset collected applying the same methodology. We have found that the results produced by Dolliver differ greatly from our replicated study. While a margin of error is to be expected, the inconsistencies we found are too great to attribute to anything other than methodological issues. The analysis and conclusions drawn from studies using these methods are promising and insightful. However, based on the replication of Dolliver's study, we suggest that researchers using these methodologies consider and that datasets be made available for other researchers, and that methodology and dataset metrics (e.g. number of downloaded pages, error logs) are described thoroughly in the context of web-o-metrics and web crawling.