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In the face of market uncertainty, mega darknet market illicit actors on the darkweb mitigate risk by displacing their operations throughout digital marketplaces. On this examine, we reconstruct market networks created by vendor displacement to study how digital marketplaces are related on the darkweb and identify the properties that drive vendor flows before and after a legislation enforcement disruption. Findings show that vendors’ movement throughout digital marketplaces creates a extremely linked ecosystem; practically all markets are instantly or indirectly related. These community traits stay stable following a law enforcement operation; prior vendor flows predict vendor motion earlier than and after the interdiction. The findings inform work on collective patterns in offender decision-making and lengthen discussions of displacement into digital areas.

INTRODUCTION

The emergence of digital marketplaces for the sale of illicit items has transformed the illicit financial system. Digital marketplaces present centralized platforms for sellers to advertise their merchandise, join with buyers, and expand their clientele. These marketplaces allow new and completely virtual transactions and complement illicit exchanges that occur offline (Leukfeldt et al. 2017).

Digital marketplaces usually are not a brand new phenomenon, but proof shows that only recently have distributors begun to displace their operations across a number of marketplaces at larger rates (Ladegaard 2020). The motion of vendors throughout digital marketplaces suggests they've develop into more and more interdependent; that's, what occurs in a single market impacts the marketplaces round it. Law enforcement interventions, together with the seizure of a market, affect surrounding markets, displacing distributors to other platforms. The circulate of ‘market refugees’ from seized to neighbouring markets has been recognized as one of many focal mechanisms via which the web economy has remained resilient to interventions (Ladegaard 2020). Vendors can maintain their on-line identities and reconnect with current and new clients on equally situated digital platforms.

Crime displacement is central to criminological scholarship. Where offenders resume their illicit activities following an intervention sheds insight into the emergence of sizzling spots and the ability to deter crime (Braga et al. 2019). Yet, we all know little about what motivates offenders’ selections to maneuver their illicit actions to a new location-physical or otherwise. Digital marketplaces provide a unique opportunity to extend discussions of crime displacement to on-line environments. Vendors, their products, and transactions often go away a record, offering mass digital traces throughout illicit marketplaces and huge populations of distributors as they unfold. Digital data from on-line marketplaces provide a unique opportunity to analyze crime displacement, permitting us to pinpoint where crime strikes to and the pathways it takes to get there. This extends current discussions of displacement and offender decision-making to incorporate the place offenders transfer to (additionally see Hatten and Piza 2021).

The present research adopts a network method to higher perceive how digital marketplaces are related by vendor displacement and assess vendors’ selections to move between markets. Specifically, we ask two interrelated questions: 1) how are digital marketplaces on the darkweb linked by way of vendor flows, and 2) does the overarching construction of the network assist explain vendor flows before and after a regulation enforcement intervention? To answer these questions, we reconstruct vendor flows across digital marketplaces on the darkweb and examine the connectivity of these marketplaces earlier than and after a significant interdiction. We then use exponential random graph fashions to determine the correlates of vendor flows and assess whether the drivers of vendor motion are disrupted following a legislation enforcement intervention. Together, the research goals to inform broader processes about crime displacement because it extends to digital spaces.

We begin with a review of digital marketplaces on the darkweb with a focus on their maturation from extra centralized to decentralized illicit economies. We then join this work with analysis on the influence of interdictions on darknet markets, theoretically grounding our discussion in rational alternative and social studying theories. We then element a mass longitudinal data collection effort to trace vendor flows throughout multiple massive-scale marketplaces and the social network methods used to examine the connectivity of this darknet ecosystem. After wanting on the aggregate patterns driving vendor flows, we consider the impression of a law enforcement seizure on vendor motion. We conclude by discussing the implications of the findings for advancing criminological idea on crime displacement and offender determination-making.

CRIME DISPLACEMENT IN DIGITAL Spaces

Digital marketplaces on the darkweb

In 2011, Silk Road turned one in every of the first giant-scale marketplaces to promote illicit items on the darkweb. Adopting a similar infrastructure to legal e-commerce websites, similar to Amazon and eBay, it set the stage for the commerce of illicit items, facilitating greater than $300k in transactions daily (Barratt 2012; Soska and Christin 2015). At its launch, Silk Road was one in every of a handful of marketplaces providing a web-based platform for illicit e-commerce; nevertheless, its success was accompanied by the emergence of competitors and its downfall much more so. Within the months following the marketplace’s seizure, a number of different marketplaces emerged to fill its void (Soska and Christin 2015), a pattern that has since continued (Van Buskirk et al. 2017).

Although digital marketplaces on the darknet are highly volatile, hardly ever surviving more than a yr (Branwen 2019), the bigger darknet economy is resilient to external shocks. Much of the scholarship on the impression of law enforcement disruptions have found the stock of illicit transactions, the volume of vendors, and the variety of markets recovers comparatively quickly after marketplace seizures. As an illustration, Décary-Hétu and Giommoni (2017) noticed that a large-scale seizure led to preliminary sharp drops within the variety of transactions and new vendors registering on e-commerce websites; nevertheless, have been restored to similar ranges inside a number of months of the intervention (also see Van Buskirk et al. 2017). Likewise, Ladegaard (2019) discovered that while a legislation enforcement crackdown led to a major reduction in the variety of obtainable markets, the stock of markets returned to the identical degree 6 months following the operation and elevated a 12 months and a half later.

Indeed, fairly than cripple the darknet economy, latest research recommend that shocks to digital marketplaces have increased their interdependency. Markets have grow to be more and more interdependent because distributors usually tend to cross-list their merchandise across multiple marketplaces. One Europol official, commenting on this phenomenon, observed that ‘[distributors] don’t just operate on one market, they cover the full spectrum of the dark web’ (Barrett 2020). In line with this statement, scholars have documented massive numbers of distributors promoting their merchandise throughout multiple marketplaces (Décary-Hétu and Giommoni 2017; Ladegaard 2019; 2020; Norbutas et al. 2020).

In one of the vital persuasive accounts of the impression of regulation enforcement interventions on vendor displacement, Ladegaard (2020) documented the widespread adoption of authentication methods throughout digital marketplaces after a significant disruption. Authentication techniques allowed marketplaces to validate vendors’ online identities, rising the convenience of shifting between markets and bringing their on-line reputations with them. Analysing vendor migration across three markets, Ladegaard (2020) discovered that many newly registered distributors had migrated from just lately seized digital marketplaces. In effect, the intervention triggered marketplaces’ adoption of authentication programs, growing the ability of illicit actors to navigate between what were as soon as unbiased marketplaces. In addition, the intervention also led to an uptick in the variety of out there directories or ‘information hubs’ that present lists of lively markets, additional rising the assets from which distributors may draw on to make knowledgeable choices on where to arrange store. These adaptations enabled illicit marketplaces to resemble authorized ones extra intently. Online identities could be verified, and customers may seek the advice of directories with up-to-date listings of energetic markets.1

Crime displacement, rational alternative and offender networks

Crime displacement, which includes where people resume their activities after an intervention, is of central theoretical importance to scholarship on crime and criminal justice. Prior research shows that crime reduction efforts usually lead to displacement (Reppetto 1976; Gabor 1981), with spatial relocation the most common response (Rossmo and Summers 2021). Where offenders move to is theoretically knowledgeable by rational alternative concept and to a sure extent, social studying theory.

Rational selection theory views offenders as determination-makers who engage in a cost-benefit evaluation of the anticipated dangers and rewards of partaking in a criminal act, together with the choice of where to offend (Becker 1968; Clarke and Felson 1993). In applying rational selection idea to the study of illicit markets, Reuter and Kleiman (1986) spotlight the salient role of perceived rewards and costs related to illicit market activity, together with earnings, incapacitation, and lack of product. Indeed, this similar economic calculus has been discovered to underlie the decision-making of actors on digital platforms, together with the decision to transition to on-line markets from offline markets, the place income are viewed as higher and dangers as decrease (Décary-Hétu and Giommoni 2017; Martin et al. 2020).

More lately, scholars have emphasised that offender resolution-making doesn't happen in a vacuum but is informed by the behaviours and actions of others. In criminology, previous work has found that friends shape the anticipated risks related to participating in crime (Stafford and Warr 1993; Pogarsky et al. 2004; McGloin and Thomas, 2016), perceived advantages (Warr 2002) in addition to the abilities and opportunities to commit crimes (Weerman 2003; Morselli et al. 2006). The function of friends in shaping offender selections is a core tenet of social learning concept, which emphasizes that people mannequin the behaviours of these round them. Indeed, social studying concept highlights friends as a key reference group from which individuals observe and study criminal and delinquent behaviours (Bandura 1978; Akers 2011). In line with social learning theories, community frameworks provide an necessary device to understand the role of peers on behaviours, with its start line the premise that individuals’ actions and beliefs depend on the actions of others in their networks (Wasserman and Faust 1994).

In illicit on-line markets, the position of offenders’ networks is obvious. Online communities provide individuals with entry to a pool of friends who inform individuals’ risk of partaking in illicit activity (Holt et al. 2008; Aldridge and Askew 2017). Past work has provided anecdotal proof that vendor decisions to move to new marketplaces are made collectively (Ladegaard 2020). Moeller et al. (2017) succinctly summarized this phenomenon with a quote from a darknet news forum, ‘If Silk Road is down, everyone strikes to Agora, if Agora is down everybody moves to Evo … and so on […] the DNM’s person base could be very herd like’ (p. 1,434). Together, these works suggest that offenders weigh the costs and benefits of illicit exercise and depend on their peer networks for informing their determination calculus, including where to sell their illicit products.

Although prior work suggests offenders’ draw from their peers to pick out illicit marketplaces, there's a notable hole in empirical work investigating exactly how friends form vendor flows throughout markets. This work means that friends function vital behavioural models, offering sources of information to guage a market’s advantages and costs. Instances where vendors see a lot of their friends on a marketplace can enhance the anticipated benefits (e.g., seeing that different distributors have chosen the platform as a useful place to conduct their business) and cut back perceived prices (e.g., signalling belief in the location as not a scam and providing a public display that they have not been arrested) (Ladegaard 2020, p. 13). Alternatively, where people see few of their peers in the marketplace could increase a site’s perceived risk and dependability. As an example, marketplaces with few of their peers may cue a site that has been planted by agents trying to observe vendor behaviours or indicating there are few buyers on these websites.

In sum, drawing from previous theoretical work that contends friends function vital behavioural fashions from which to observe and be taught offending behaviours, and newer work that finds illicit market individuals draw from their friends to evaluate the prices and benefits of illicit actions, we expect vendors’ friends to play an vital function in shaping on-line behaviours. Specifically, we expect distributors to maneuver to marketplaces the place their friends have moved to in the past, leading to the following speculation:

Hypothesis 1: Vendor flows usually tend to happen between marketplaces the place vendors’ peers have moved to prior to now.

Further, drawing on previous research that emphasizes disruptions enhance vendor motion across marketplaces, we expect this relationship to strengthen following a regulation enforcement intervention. We'd count on the anticipated costs of collaborating in illicit activity to be heightened with increased consideration from legislation enforcement. In these contexts, distributors may be more threat-averse and more prone to depend on their peer network to determine trusted websites, following those distributors who weren't detected prior to now shutdown. Indeed, prior work has shown that reputation and trust take on a better market value after a disruption (Duxbury and Haynie 2020). This line of labor led to the following hypothesis:

Hypothesis 2: A legislation enforcement disruption will strengthen the relationship between present vendor flows and where distributors peers’ have moved to up to now.

Examining VENDOR FLOWS BETWEEN DIGITAL MARKETPLACES

The current examine empirically checks these hypotheses by reconstructing vendor flows across digital marketplaces before and after a significant legislation enforcement interdiction. Prior analysis on crime displacement has primarily focused on whether interventions cut back crime or relocate it to different areas (Hatten and Piza 2021). Here, we examine a big pattern of offenders and explore the properties that lead them to move to particular online areas. In doing so, we search to maneuver the scholarship on crime displacement forward, substantively and methodologically, by taking a look at how vendor motion connects digital marketplaces and assessing how the construction of market networks shapes collective patterns in offender resolution-making.

Theoretically, our examine attracts from rational choice and social studying theories to better perceive offender decision-making and crime displacement in online areas. While early scholars emphasised the necessity to check where (and when) crime occurs (Felson 2006), a lack of detailed information precluded these efforts. The digital panorama affords a new source of data to analyze offenders’ alternative constructions and supply perception into the fundamental determinants of offender displacement patterns. In the present study, we explicitly test whether vendors’ decisions on where to promote their products is modelled off the behaviour of their friends. Our outcomes shed gentle on the processes via which distributors move to different illicit marketplaces, with a focus on the economic and social forces that construction these decisions.

Methodologically, a community method permits us to explore questions central to scholarship on crime displacement. The questions being raised on online platforms should not new. Crime displacement has been studied for many years, with a lot of this literature specializing in the impact of crime discount efforts on the motion of crime to new areas (Weisburd et al. 2006; Braga et al. 2019), and more moderen applications on the place offenders transfer to (Hatten and Piza 2021). Specifically, we conceive of marketplaces as a network wherein individual e-commerce sites are nodes, and the motion of sellers between sites are edges. We then use exponential random graph models to examine the drivers of vendor motion earlier than and after a legislation enforcement seizure of certainly one of the biggest markets. In doing so, we present how a network method gives a novel lens by which to discover the etiology of crime displacement.

DIGITAL Trace Data ON THE DARKWEB

The data for this paper comes from English-language marketplaces that promote stolen data merchandise hosted on the darkweb. Stolen knowledge products are outlined right here as fraudulent documents (e.g., drivers’ licenses, passports), monetary objects (e.g., financial institution accounts, credit playing cards), counterfeit currencies, providers to steal information (e.g., account crackers, injectors), and tutorials or guides related to any of the preceding classes. Because some markets don't classify product listings or misclassify listings, we used a set of keywords to extract the relevant listings for the analysis (see Appendix I for a full listing of key phrases). The info only contains marketplaces with multiple vendor and more than a hundred stolen data listings.

Marketplaces assembly these standards were identified by consulting marketplace directories, web sites that checklist active markets on the darkweb and the onion.links to entry them. These websites present a beneficial useful resource for vendors and consumers to identify up-to-date data on markets, together with their hyperlinks, as markets could switch their onion.link in efforts to elude regulation enforcement or other hostile actors. As well as, marketplaces had been positioned by consulting popular boards on the darkweb for discussions of recent markets. Digital data from every market have been then compiled right into a structured database using web-scraping and parsing instruments that extracted all publicly obtainable product listings, and vendor profiles pertaining to stolen data objects (Wu et al. 2019). Our ultimate pattern includes 17 markets, 979 distinctive vendor aliases, and 221,094 product listings over an approximately 12-week period from 15 November 2020, to 9 February 2021.

Methodological boundaries largely explain why prior analysis on the networks of digital markets is limited. To evaluate vendor flows requires capturing vendor exercise across a large sample of digital marketplaces, demanding data throughout a number of platforms, every with 1000's of knowledge points with different infrastructure that can change over time. Because few comprehensive longitudinal datasets throughout multiple markets exist, these analyses have but to be carried out. However, gathering information from a number of markets creates empirical obstacles, and the limits to our method should be famous.

First, marketplaces on the darkweb are notoriously unstable. Markets usually go down for maintenance and aren't accessible for extended periods. Because of this, we knowingly omit some listings if the market went down throughout the scraping period. Our data collection approach partially overcomes this limitation, as we scraped the markets weekly and then aggregated this information over 4 weeks, offering more comprehensive knowledge points. However, we may be missing listings that went up and then had been taken down inside shorter time intervals. Relatedly, we additionally confronted points with our own scrapers with the seized market, DarkMarket, not fully scraped within the three weeks previous to it being shut down.

One different limitation that would probably affect our analysis needs to be noted. Our data solely incorporates info on vendors’ on-line aliases. It is feasible that vendors use completely different aliases across marketplaces or that aliases are ‘mimicked’ by others in efforts to rip-off buyers, and there is a few proof of this impact (van Wegberg and Verburgh 2018; Martin et al. 2020). However, latest work suggests that the adoption of vendor verification processes by webpage administrators has limited this possibility (Ladegaard 2020; Norbutas et al. 2020), and others have shown that vendor aliases function a valid proxy for figuring out vendors’ unique identities (Broséus et al. 2016; van Wegberg and Verburgh 2018). Indeed, vendors’ aliases present ‘brand recognition’, and are immediately tied to their online reputations, one of the principle ways prospects choose sellers (Duxbury and Haynie 2018). Although not excellent, in the absence of extra reliable approaches we comply with past work (Décary-Hétu and Giommoni 2017; Ladegaard 2018; 2019) and deal with every vendor alias as unique. In doing so, we are conservative in our approach, requiring exact matches of vendor aliases to be categorized as the same vendor.

To help interpret our quantitative findings, we additionally reached out to distributors to conduct interviews on the factors that structured their decisions to arrange storefronts on digital marketplaces. We recruited vendors who made at least one sale on a darknet market within the month previous the recruitment message. In complete, 865 distinctive distributors fitting these standards were identified. As a consequence of market volatility, our analysis crew was only in a position to contact 360 vendors throughout 12 markets between four April 2021 and 1 May 2021 and requested to participate in an asynchronous interview on an encrypted platform of their choice. Follow-up messages had been despatched two weeks after the primary participation request. From the 360 vendors contacted, twelve replied. Of those twelve, one completed the total interview, and one accomplished a partial interview. Content from these interviews is integrated to provide insight into the choice-making processes underpinning vendor movement; nonetheless, we emphasize our restricted sample, which we return to in the restrictions.

ANALYTIC Approach

Our analysis focuses on the social networks created by vendor flows through which the nodes characterize markets, and the ties signify the inventory of distributors who move between any set of markets. Conceptualizing and measuring vendor flows as market-degree social networks permits us to evaluate the structural options of the network and permit the analyses of the mechanisms driving the construction of the observed market community. We measure the market networks in the 1-month interval before and after the seizure of one among the biggest marketplaces on the darkweb-DarkMarket. We start by describing the structural traits of the market networks, together with stability in these structures over time. This includes properties of the community graph comparable to its overall clustering (density), local clustering (clustering coefficient), and the extent to which vendor movement is centralized around a few key markets (diploma centralization). We then use exponential random graph models (ERGMs) to examine the local processes that form international patterns in the structure of vendor flows, and whether these processes change earlier than and after the market seizure.

Seizure of DarkMarket

The seizure of the DarkMarket on 11 January 2021, by Europol authorities closely resembles an extended line of enforcement interventions geared toward curbing illicit exercise on the darkweb. At the time of its operation, DarkMarket was identified as certainly one of the biggest marketplaces for illicit items on the darkweb (Europol 2021). Overnight, the positioning was taken down, with law enforcement seizing the servers that hosted the website and arresting the alleged operator of the market. Its takedown supplies a unique alternative to check how an intervention impacts vendor flows across markets and is in keeping with other studies which have tested the impression of regulation enforcement interventions on digital marketplaces (van Buskirk et al. 2017; Décary-Hétu and Giommoni 2017; Ladegaard 2019).

Dependent variable: vendor flows between digital marketplaces

The dependent variable measures the depth of vendor flows between any two sets of digital marketplaces involved in the sale of stolen data. The networks are two-mode network affiliation knowledge that records all markets a vendor advertised stolen knowledge merchandise (vendor-by-market) and the dates they were recorded as listing these merchandise. The affiliation networks are then transformed into networks of co-affiliation by creating a brand new matrix that information the number of distributors who moved between any pair of markets. The resulting information is a one-mode network (market-by-market) with the identical market listed within the rows and columns of the matrix. The worth of each cell available in the market matrix signifies the number of distributors who handed from the sender market (rows) to the receiver market (columns), permitting us to determine the inventory of distributors who listed stolen knowledge products in one market (Market A), after which began itemizing stolen knowledge merchandise on one other (Market B). As such, markets are related if 1) a vendor expanded the number of marketplaces they're on (listed products on Market A at time t and then listed merchandise on Market B at time t + 1), or 2) a vendor left a marketplace and joined a brand new one (discontinued itemizing merchandise on Market A at time t and then started itemizing merchandise on Market B at time t + 1). Thus, ties between markets are directed and valued, indicating the course of the vendor flow and the depth of the movement, with extra vendors transferring between any two units of markets having increased values. We measure our dependent variable at two time factors, 1 month before the seizure of DarkMarket (pre-seizure community) and 1 month after the seizure of DarkMarket (publish-seizure community).

To manage for the fact that certain markets might have larger opportunity for larger out-flows based on the full vendor population on that market, we measure vendor out-flows because the number of vendors who transfer from the market as the proportion of all vendors available on the market at time t. After calculating the ratio of market out-movement to the market vendor inhabitants throughout all pairs of markets, we use quartiles to find out thresholds between markets that send few distributors and those that ship many distributors. The quartiles classify the edges into categories based on the depth of vendor flows, with decrease values indicating a lower proportion of out-circulation and better values indicating a higher proportion of out-move. This approach was adopted from analyses of human migration networks to control for nations of different sizes (Vogtle and Windzio 2016).

Exponential random graph fashions

While the network statistics permit us to describe patterns in vendor flows, the usage of ERGMs permits us to test 1) the mechanisms that drive the formation of the market networks and 2) the influence of a regulation enforcement interdiction on disrupting the construction of vendor flows between markets. ERGMs mannequin the chance of tie formation throughout the observed network as a function of each actor attributes and characteristics of the network itself. ERGMs are uniquely suited to answer our analysis question, as they supply a method to beat the problem of endogeneity that's inherent to network data and thus violates assumptions of conventional regression techniques (Robins et al. 2012). ERGMs resolve the problem of non-independence by explicitly modelling how one network tie influences the chance of different network ties (Lusher et al. 2013). Further, ERGMs permit us to explicitly take a look at peer results by including network features as covariates within the model. This is vital to the current research, which aims to instantly check whether patterns in vendor displacement are influenced by the behaviours of other distributors.

The longitudinal nature of the data offers two analytical approaches for modelling change in the market networks: 1) a temporal ERGM (TERGM) with binary network information, or 2) two separate ERGMs (pre- and submit-seizure) with valued community knowledge and a lagged dyadic covariate for prior community construction. The primary choice, TERGMs extend standard ERGMs by modelling the extent to which the edges (and non-edges) are stable across observations. However, current purposes of TERGM are restricted to binary information, and thus would potentially treat markets with excessive and low volumes of vendor out-flows as equivalent, conflating very different market profiles. In distinction, the second option, valued ERGMs, extends commonplace ERGMs by additionally modelling whether or not a covariate increases or decreases the value of an edge between network actors (Krivitsky 2012). As such, valued ERGMs allow us to assess not solely which markets expertise vendor flows but in addition the depth of those flows, permitting us to measure the inventory of vendor motion throughout markets.

Valued ERGMs require specifying a reference distribution to model how edge values are distributed amongst community actors. Here, we use a Poisson-reference distribution to model the overall community (Krivitsky 2012). We estimate the probability and intensity of ties forming between markets using two lessons of predictors: nodal covariates and structural covariates. Nodal covariates test whether actor attributes impact their chance of receiving or forming a tie and the depth of that tie. Nodal covariates are dyad impartial because the probability any pair of nodes could have a community tie is determined by their attributes but isn't conditional on other community ties. Structural covariates test whether or not properties of the community itself impression the likelihood any pair of nodes may have a network tie and the depth of that tie. Structural covariates are dyad dependent, with the likelihood of a tie being modelled as conditional on different community ties. Together, these covariates provide completely different insights into the native processes that dictate collective patterns in vendor flows.

Nodal covariates

Number of vendors is a measure of the number of distinctive vendor aliases on the marketplace at time t. This measure serves as a proxy of market provide and is theoretically knowledgeable by rational selection perspectives, which contend that economic calculations, including supply and demand, drive illicit activity on and offline (Reuter and Kleiman 1986; Aldridge and Décary-Hétu 2016; Demant et al. 2018; Décary-Hétu and Giommoni 2017). We might anticipate higher supply (i.e., more vendors) to cut back the chance distributors would be a part of an already aggressive market. However, we also recognize that the number of distributors may also impression vendors’ danger evaluation for joining the market, unbiased of monetary concerns. Indeed, past work has proven the presence of others impacts the choice to have interaction in illicit activity, rising an individual’s perceived anonymity and decreasing the anticipated sanctions with participating within the activity (McGloin and Thomas 2016). Thus, is it additionally potential to conceive that markets with extra vendors will appeal to additional distributors to the market.

Price change is a measure of the extent to which listing costs change on the marketplace at time t. We measure the typical price change of a product listing by taking the same itemizing and evaluating its price at weekly intervals. We measure this throughout all listings after which take the average over the four-week period, offering the average price change throughout product listings on the market. This measure serves as a proxy of a marketplace’s demand, an approach in line with other studies (Décary-Hétu and Giommoni 2017). Similar to our measure of market supply, we draw from the rational alternative perspective that shows distributors are motivated by financial incentives (Reuter and Kleiman 1986; Martin et al. 2020). We thus count on distributors to be extra attracted to marketplaces with increases in demand (price increases) and fewer attracted to marketplaces with drops in demand (price decreases).

As well as, directed networks offer the chance to research how market covariates influence the likelihood of sending ties or receiving ties. Thus, for both nodal covariates described-number of distributors and value change-we look at the impact of the nodal attribute on out-degree (the likelihood a market will ship high out-flows of vendors to other markets), and in-degree (the probability a market will obtain high in-flows of distributors from different markets), permitting us to disentangle vendor decisions to depart outdated markets, from vendor choices to hitch new ones.

Network covariates

Density is modelled utilizing the sum parameter, which indicates the expected worth of a tie between any pair of markets based on the value of all noticed network ties (Handcock et al. 2021). The sum term is analogous to an intercept in customary regression strategies, reflecting the baseline edge worth throughout community actors.

Reciprocity is modelled utilizing the mutual time period, which estimates the probability a tie between any pair of community nodes will likely be reciprocated (Handcock et al. 2021). That is, the extent to which distributors from Market A transfer to Market B also influences whether or not vendors from Market B transfer to Market A. Reciprocity is a properly-established community course of that can affect network structure, serving as an necessary control for estimating structural processes.

Transitivity is measured utilizing the transitiveweights term, which examines whether or not a tie value in the network could possibly be explained by triad closure. Transitivity occurs in networks when ties between two sets of actors improve the chance of a tie between a 3rd actor. In our case, transitivity permits us to check whether vendor flows are doubtless to move between markets that have a tie in frequent, and thus whether clustering dictates how vendors’ move between markets. Prior analysis on criminal networks has observed that illicit networks usually tend to adopt decentralized and safe constructions following a legislation enforcement intervention (Morselli, Giguère and Petit, 2007; Ouellet et al. 2017). However, recent work on digital marketplaces has instructed that distributors are more likely to displace their operations following a market seizure, which might counsel that they become more related and less secure. A negative impact for this term this would support the former speculation (more secure structures), while a optimistic impact would help the latter speculation (extra environment friendly buildings) with greater clustering in network ties.

Prior community construction is our most important covariate and is modelled utilizing a dyadic covariate term, which entails the adjacency matrix of vendor flows within the preceding 4-week period, i.e., a lagged dependent variable. The dyadic covariate time period permits us to test the hypothesis that vendor flows are more likely to happen between markets wherein they've occurred prior to now and whether or not a law enforcement operation strengthens or interrupts this peer effect. A optimistic and statistically significant effect would counsel that vendor flows are structured by the place their friends moved up to now. Should this effect turn into stronger in the publish-seizure community, this could counsel vendors enhance their reliance on their friends to determine where to sell their illicit products.

Results

We present our ends in two stages. The first stage describes the structural options of the digital marketplaces before and after a regulation enforcement seizure. The first stage goals to determine the extent to which vendor flows join the various marketplaces and the options of these networks. The second stage explains the generative processes that led to the observed networks, presenting the results from the ERGMs. The second stage aims to determine the essential explanatory variables related to vendor displacement across markets and whether this changes following a disruption. Across both sections, we complement quantitative findings with accounts from our interviews with distributors.

How vendor flows join digital marketplaces

Figure 1 depicts the market networks before and after a regulation enforcement seizure. Each node in the network represents a market concerned within the sale of stolen information on the darknet. The scale of the node indicates the extent to which distributors moved to that market: larger nodes sign markets that received vendor flows from a greater variety of markets. The edges present the depth of the vendor flows between markets, with thicker edges representing a better stock of distributors moving between these markets and arrows indicating the path of the flows.

Vendor flows between digital marketplaces on the darknet. Notes: Node measurement indicates a market’s in-diploma. Edge width captures the depth of vendor flows, with thicker edges indicating a better quantity of distributors flowing between any pair of markets and arrows the direction of the stream. One isolate in the pre-seizure network, Yakuza Market, isn't shown.

Figure 1 highlights two key features of the community. First, digital marketplaces on the darkweb are extremely linked. The stream of vendors across digital marketplaces creates a community that hyperlinks nearly all markets into a single component. Nearly all marketplaces are straight or not directly related to one another by way of vendor flows. Second, this connectivity persists earlier than and after a major legislation enforcement intervention. Together, this figure supplies a primary look at the construction of vendor flows throughout digital marketplaces, exhibiting the linked nature of the darknet ecosystem.

Table 1 presents the descriptive statistics for the market networks, offering a more detailed understanding of how vendor flows are distributed across the community. The pre-seizure market network consists of 17 markets and 95 ties connecting them. A community density of 0.349 earlier than the seizure of DarkMarket indicates that 35 % of all possible ties between community actors are noticed in the market. The clustering coefficient appears on the native connectivity of the market community, the extent to which ties are clustered around actors. A clustering coefficient of 0.676 suggests that there's a comparatively high degree of clustering within markets. Degree centralization indicates whether or not community ties are concentrated around a couple of central actors, with greater values indicating larger concentrations (Freeman 1978). In-diploma centralization captures the extent to which a few markets obtain nearly all of ties. In distinction, out-degree centralization captures the extent to which just a few markets ship the vast majority of ties. Prior to the seizure, markets that acquired distributors tended to be more centralized with an in-diploma centralization of 0.401. In distinction, markets that despatched vendors tended to be slightly more distributed across marketplaces, with an out-diploma centralization of 0.276.

The network construction of vendor flows between digital marketplaces on the darkweb

According to the pre-seizure community, the post-seizure community consists of 17 markets, however they're better related with the next variety of ties between them, 122 edges as in comparison with 95 edges earlier than the seizure. Although DarkMarket was seized, we embody it in the post-seizure community to observe the out-flow of vendors to different markets. The publish-seizure market community becomes extra linked, with the density rising to 0.449 and the clustering coefficient to 0.838, as compared to the pre-seizure network. This suggests that vendor flows became more dispersed, with distributors connecting extra of the markets, a finding per prior work that means vendor flows elevated following an intervention (Ladegaard 2020). While out-diploma centralization will increase slightly across the pre- and put up-seizure interval, in-degree centralization drops slightly in the publish-seizure period. This suggests markets sending distributors develop into slightly more concentrated round a number of markets, in keeping with the takedown of DarkMarket and enormous outflows from this market. In addition, vendor in-flows develop into slightly much less centralized; the community determine confirms this, highlighting a bigger core group of markets that acquired greater vendor in-flows after the law enforcement seizure.

The tendency for distributors to move across multiple platforms might be seen in a single vendor’s account of how they select which marketplaces to promote their products: ‘I initially acquired grandfathered into one in every of the top markets locations also known as white home market, thats where all the true players are. From white house i used to be in a position to get vendor bond waived on virtually each other market place’. Another vendor emphasized that having a number of storefronts minimized any concerns about a market going down: ‘i have loads of backup storefronts already active and my customers will understand how to find me not tremendous tough.’ This finding confirms what has been found by others, setting up store across a number of marketplaces is facilitated by market administrators (waiving vendor charges for established vendors), and is a technique for vendors’ coping with the volatility of markets. In the subsequent part, we explore the processes that lead distributors to pick particular marketplaces.

The drivers of vendor flows on the darkweb

Table 2 introduces the results for the Poisson ERGMs, which mannequin the depth of vendor flows between any pair of markets. We estimate two models: the predictors of vendor flows pre-seizure (left) and vendor flows put up-seizure (proper). For both units of fashions, we embody the identical set of nodal and structural covariates. For the pre- and publish-seizure networks, the prior community construction term entails the lagged adjacency matrix of vendor flows in the prior four-week period. Within the submit-seizure community, this time period entails the adjacency matrix of the pre-seizure community. In the pre-seizure network, this term entails the adjacency matrix of the market community four weeks previous to the pre-seizure community.

Poisson exponential random graph fashions predicting vendor flows between digital marketplaces

***p < 0.001, **p < 0.01, *p < 0.05, †p < 0.10.

Table 2 reveals that vendor flows prior to the seizure of DarkMarket had been guided by the variety of distributors and prices. The unfavorable and significant effect for the variety of vendors-sending market signifies that markets with extra vendors have been less prone to experience out-flows of vendors to different marketplaces. The finding that distributors are less likely to move away from markets with a excessive variety of distributors aligns with past work, which observes individuals’ perceptions of dangers decreases when extra friends are present (McGloin and Thomas 2016). The negative and important effect for worth change-sending market indicates that markets that had a drop in itemizing prices have been more more likely to expertise out-flows of vendors to other markets. The discovering that markets with a drop in demand are in step with core tenets of rational choice and the effectively-established discovering that offender resolution-making is structured by financial motives (Reuter and Kleiman 1986; Martin et al. 2020). Together, these results show that market components formed vendor choices to displace their operations however not the place they selected to move to.

When it comes to the network variables, the reciprocity time period had a negative and vital effect, showing that out-flows of vendors to other markets tended to not be reciprocated from the receiving market. However, the transivity term had null results on vendor flows, showing no clustering within the pre-seizure network. In support of our essential speculation, the network lag term-prior community construction-had a optimistic and important effect, indicating that the movement of vendors between markets was guided by the collective patterns of where people had moved in the past.

The mannequin of vendor flows after the seizure of DarkMarket, suggests a change in vendor preferences for choosing marketplaces. Specifically, we observe positive and vital effects for each the number of distributors-receiving market and the variety of distributors-sending market, showing that marketplaces with a higher variety of distributors had been more likely to expertise out-flows and in-flows of vendors. Thus, after a significant market was seized, vendors responded by shifting to markets where there were extra distributors; nonetheless, additionally they left markets that had greater numbers of distributors. The previous result is according to theoretical expectations that vendors would transfer to sites where there were more vendors, doubtlessly signally greater anonymity, where they had been less more likely to be singled out and hidden inside a larger group. However, the discovering that vendors left markets with a better number of distributors contrasts with what we found within the pre-seizure market, probably suggesting that the disruption might have made vendors extra risk-averse to stay on the same market, and more inclined to develop their operations.

Price changes remained a big issue for shaping vendor out-flows and helped explain vendor in-flows in the put up-seizure models. After the seizure of DarkMarket, there was a detrimental and significant impact for each worth change-receiving market and price change-sending market, indicating distributors were more probably to move to and from markets that had drops in costs. Although counterintuitive at first, this discovering may even be partially explained by the tendency for vendors to search for their own offers, which they'll then resell. As an example, one vendor defined, ‘If I see something that’s a very good deal i will purchase it just for the only intention to resell however at all times bulk listings clearly, that’s how you earn a living.’ From this perspective, distributors may be attracted to marketplaces from which they can even supply their merchandise extra efficiently.

In step with the pre-seizure model, the reciprocity time period is destructive and important, indicating that vendor flows were not reciprocated across marketplaces after the intervention. In contrast to the pre-seizure model, the transitivity time period is optimistic and significant, indicating that after the seizure there was clustering of vendor flows between markets, with vendors extra seemingly to maneuver to markets that had a shared market in widespread. This result's in line with our descriptive findings that showed the network became extra clustered following the regulation enforcement seizure. Lastly, according to the pre-seizure community, the prior network construction time period remains optimistic and significant. This provides assist for our first hypothesis that vendors had been more probably to maneuver to markets that their friends had moved to in the past. However, we do not discover proof for our second speculation, which anticipated this relationship to turn into stronger within the post-seizure network. Rather, we find that vendor flows stayed comparatively stable earlier than and after the intervention.

Discussion

In the current examine, we discover that digital marketplaces on the darkweb are extremely related through vendors who span multiple platforms. Further, we observe that distributors do not randomly select into markets, and these micro-preferences produce aggregate stage patterns that generate the ecosystem’s construction. Below we detail the main findings of our examine and focus on how they construct on prior theoretical and empirical work on offender networks and displacement.

The current research extends investigations of crime displacement and offender determination-making to indicate that the place offenders decide to commit their crimes is shaped by their peers. Vendors have been more possible to pick into marketplaces the place their peers had moved to up to now, and this discovering stayed constant earlier than and after a law enforcement disruption. This end result aligns with bigger propositions from social studying theory that emphasize the role of friends in offender choice-making (Akers 2011). Although our knowledge do not allow us to uncover the mechanisms that underlie peer results, prior analysis offers some clues. Peers shape the perceptions of prices and benefits of deviance, together with perceived sanction threat (Stafford and Warr; 1993; Pogarsky et al. 2004; McGloin and Thomas 2016) and the anticipated rewards (Warr 2002). In digital marketplaces, distributors observing their peers transfer to another market could present cues that the market is reliable. Indeed, students have long emphasised that a dominant driver of illicit market exercise is trust, with patrons extra possible to buy products from reliable vendors, extra so than the cost of the products being bought (Duxbury and Haynie 2018, additionally see Diekmann et al. 2014), and reputation takes on a better market worth after a disruption (Duxbury and Haynie 2020). Our results suggest that just as buyers decide up cues on reliable sellers from different buyers’ experiences, distributors additionally depend on their networks to assess which markets are reliable on which to sell their wares. In essence, seeing their friends move to a new market serves as an endorsement of the platform.

In addition, our study’s findings showed that market networks became more connected after a legislation enforcement intervention, a end result that runs counter to the properly documented finding that illicit networks tend to adopt extra secure and decentralized buildings within the face of risk and uncertainty (Morselli et al. 2007; Ouellet et al. 2017). The totally different responses of criminal networks throughout offline and contexts could also be partially defined by the anonymity afforded by the darknet. A key consideration as to whether or not a community will adopt safe buildings hinges on if they have access to trusted contributors or depend upon extra dangerous affiliates (Morselli et al. 2007). When threat will increase, individuals may protect themselves by adopting extra safe network positions the place they're less dependent (or connected) to those much less trusted others. In on-line markets, an individual’s identity remains hidden to the market contributors, and thus their networks are less topic to considerations that predominate offline criminal exercise. In these nameless contexts, distributors more closely resemble sellers on licit e-commerce sites, counting on online critiques and rankings to ascertain the standard of their merchandise. When markets change into extra risky, vendors can mitigate dangers by already having established a storefront on another platform the place their distributors can simply find them. Indeed, considered one of our vendor interviews emphasised that establishing multiple storefronts provide ‘backups’, allowing them to mitigate the loss from market closures.

Lastly, we observe that financial calculus drives offenders’ selections on where to promote their merchandise online. Specifically, we discovered that vendors had been extra probably to maneuver to and from marketplaces that lately experienced drops in demand. The discovering that distributors transfer from marketplaces that experienced drops in demand is in keeping with a rational choice perspective that identifies monetary components as weighing heavily in offender resolution-making, with the purpose of maximizing income (Reuter and Kleiman 1986). However, the discovering that vendors move to marketplaces that also expertise drops in demand runs counter to this logic. While counterintuitive at first, this will indicate that vendors who had been experiencing a lower in demand determined to expand their research to other markets, according to prior analysis which has found distributors on multiple markets usually tend to reap greater profits (Ladegaard 2020; Norbutas et al. 2020), and vendor interviews expressing how lower prices allow vendors to capitalize by reselling these products on their very own terms. Vendors may absorb these costs in the short-term, establishing themselves on the platform on the assumption that demand will resume later, a proposition per past work (Décary-Hétu and Giommoni 2017).

Limitations

Our study depends on distributors involved in the sale of stolen data merchandise on digital marketplaces on the darkweb. Stolen knowledge items are the second largest class of illicit products on darkweb marketplaces (after drugs); however, they only represent a subset of all illicit on-line listings (Hutchings and Holt 2015). While we can capture a high number of markets, we shouldn't have information on all vendors lively on these markets, or all markets lively on the darkweb and clearnet. Limiting our analysis to the subset of merchandise on the darkweb gives the mandatory infrastructure to compare a number of vendors using the same variables; nonetheless, this might doubtlessly obscure some patterns that may be observed in different settings, and thus findings apply primarily to this context.

Further, our evaluation solely focuses on the impression of a single shock to digital marketplaces on the darkweb-the seizure of DarkMarket on eleven January 2021. However, this solely captures certainly one of many law enforcement interventions on the darknet. Earlier interventions, together with the shutdown of Empire market in August of 2020, should be creating waves on the darknet where markets and distributors are recovering from these earlier shocks. Relatedly, while darknet marketplaces provide troves of data on illicit transactions, they miss knowledge on among the core covariates of criminality, together with offender backgrounds, reminiscent of sex, and age, which can impression decisions to offend, and where they resolve to commit their offences.

Lastly, we emphasize that our interviews depend on a small sample. Our low response fee could also be a operate of our sampling body, recruitment strategy, or a mixture of each. Vendors who sell stolen knowledge products on the darknet could understand the risks related to being interviewed as outweighing the rewards. Thus, it's our perception the response fee could possibly be improved by rising the rewards (incentivizing participants) or decreasing the perceived dangers (establishing trust and credibility) of participation. As well as, we also take notice of the small samples of current analysis adopting comparable approaches, including the largest sample of qualitative interviews being thirteen distributors selling drugs on these platforms (Martin et al. 2020). Strategies, comparable to creating rapport in on-line spaces, including partnering with established web sites, might partially explain the discrepancies, and we encourage additional work on this area.

CONCLUSION

Our research advances a network framework to understand digital marketplaces as an ecosystem. Drawing from information across a number of marketplaces, we confirmed illicit marketplaces are highly connected via vendors who transfer between totally different platforms, and that these networks grew to become more linked after a disruption. Investigating the local mechanisms that drove the construction of the market community, we observed that economic issues including fluctuations in market demand structured vendor flows between markets. We additionally found that vendor flows had been extra more likely to happen between marketplaces where their peers had moved to prior to now. Together, our research demonstrates the significance of economic and social forces, together with peers’ behaviours, to better perceive crime displacement and offender decision-making.

APPENDIX I. Online Stolen Data Key Words

Footnotes

It's important to note that the increase in ease with which distributors can transfer between digital platforms has resulted in two distinct but associated phenomenon: 1) vendors’ cross-use of platforms (situations where distributors advertise their merchandise throughout multiple marketplaces), and 2) vendors’ migration across platforms (instances where distributors move their product listings from an old market to a new market). While vendors’ cross-use of platforms and migration signify distinct phenomena, they overlap considerably. Indeed, the volatility of darknet marketplaces has led to increases in vendors operating out of multiple marketplaces and ‘refugees’ who move to new markets once one has shut down. Both phenomena symbolize motion patterns, where an offender may move to further sites to mitigate danger and broaden their operations, and each phenomena enhance the connectivity and dependency between marketplaces. Within the remainder of this text, we use the time period vendor motion and circulate to capture cases where vendors increase their operations or relocate to new markets.

FUNDING

This materials is predicated upon work supported by the U.S. Department of Homeland Security below Grant Award Number 17STCIN00001-05-00. The views and conclusions contained on this document are these of the authors and shouldn't be interpreted as necessarily representing the official policies, both expressed or implied, of the U.S. Department of Homeland Security.

REFERENCES

Akers, R. (2011), Social Learning and Social Structure: A General Theory Of Crime And Deviance, Transaction Publishers.

Aldridge, J., and Askew, R. (2017), ‘Delivery Dilemmas: How Drug Cryptomarket Users Identify and Seek to scale back Their Risk of Detection by Law Enforcement’, International Journal of Drug Policy, forty one: A hundred and one-109.

Aldridge, J., and Décary-Hétu, D. (2016), ‘Hidden Wholesale: The Drug Diffusing Capacity of Online Drug Cryptomarkets’, International Journal of Drug Policy, 35: 7-15.

Bandura, A. (1978), ‘Social Learning Theory of Aggression’, Journal of Communication, 28(3): 12-29.

Barratt, M. J. (2012), ‘Silk Road: Ebay for Drugs’, Addiction, 107(three): 683-683.

Barrett, B. (2020), ‘179 Arrested in Massive Global Darkweb Takedown’, Wired. Retrieved June 13, 2021. Available at: https://www.wired.com/story/operation-disruptor-179-arrested-world-dark-internet-takedown/.

Becker, G. S. (1968), ‘Crime and Punishment: An Economic Approach’, Journal of Political Economy, 76: 169-217.

Braga, A. A., Turchan, B. S., Papachristos, A. V., and Hureau, D. M. (2019), ‘Hot Spots Policing and Crime Reduction: An Update of an Ongoing Systematic Review and Meta-Analysis’, Journal of Experimental Criminology, 15(3): 289-311.

Branwen, G. (2019), ‘Darknet Market Mortality Risks’, Gwern.Net. Retrieved June 13, 2021. Available at: https://www.gwern.net/DNM-survival.

Broséus, J., Rhumorbarbe, D., Mireault, C., Ouellette, V., Crispino, F., and Décary-Hétu, D. (2016), ‘Studying Illicit Drug Trafficking on Darknet Markets: Structure and Organisation from a Canadian Perspective’, Forensic Science International, 264: 7-14.

Clarke, R. V. G., and Felson, M. (1993), Routine Activity and Rational Choice. Transaction Publishers.

Décary-Hétu, D., and Giommoni, L. (2017), ‘Do Police Crackdowns Disrupt Drug Cryptomarkets? A Longitudinal Analysis of the consequences of Operation Onymous’, Crime, Law and Social Change, 67(1): Fifty five-75.

Demant, J., Munksgaard, R., and Houborg, E. (2018), ‘Personal Use, Social Supply or Redistribution? Cryptomarket Demand on Silk Road 2 and Agora’, Trends in Organized Crime, 21(1): 42-sixty one.

Diekmann, A., Jann, B., Przepiorka, W., and Wehrli, S. (2014), ‘Reputation Formation and the Evolution of Cooperation in Anonymous Online Markets’, American Sociological Review, seventy nine(1): Sixty five-eighty five.

Duxbury, S. W., and Haynie, D. L. (2018), ‘Building Them up, Breaking Them Down: Topology, Vendor Selection Patterns, and a Digital Drug Market’s Robustness to Disruption’, Social Networks, fifty two: 238-250.

Duxbury, S. W., and Haynie, D. L. (2020), ‘The Responsiveness of Criminal Networks to Intentional Attacks: Disrupting Darknet Drug Trade’, PLOS ONE. 15(9): e0238019.

Europol. (2021), ‘DarkMarket: World’s Largest Illegal Darkweb Marketplace Taken Down.’, Europol. Retrieved June 16, 2021. Available at: https://www.europol.europa.eu/newsroom/information/darkmarket-worlds-largest-illegal-darkish-internet-marketplace-taken-down.

Felson, M. (2006). Crime and Nature. SAGE Publications.

Freeman, L. C. (1978), ‘Centrality in Social Networks: Conceptual Clarification’, Social Networks, 1: 215-239.

Gabor, T. (1981), ‘The Crime Displacement Hypothesis: An Empirical Examination’, Crime & Delinquency, 27(3): 390-404.

Handcock, M., Hunter, D., Butts, C., Goodreau, S., Krivitsky, P., and Morris, M. (2021), ergm: Fit, Simulate and Diagnose Exponential-Family Models for Networks. The Statnet Project (https://statnet.org). R package version 4.0.1. Available at: https://CRAN.R-challenge.org/package deal=ergm.

Hatten, D., Piza, E. L. (2021), ‘When Crime Moves Where Does It Go? Analyzing the Spatial Correlates of Robbery Incidents Displaced by a place-Based Policing Intervention’, Journal of Research in Crime and Delinquency, Online First.

Holt, T. J., Blevins, K. R., and Kuhns, J. B. (2008), ‘Examining the Displacement Practices of Johns with On-Line Data’, Journal of Criminal Justice. 36(6): 522-28.

Hutchings, A., and Holt, T. J.. (2015), ‘A Crime Script Analysis of the net Stolen Data Market’, British Journal of Criminology, fifty five(three): 596-614.

Krivitsky, P. N. (2012), ‘Exponential-Family Random Graph Models for Valued Networks’, Electronic Journal of Statistics, 6: 1100-1128.

Ladegaard, I. (2018), ‘We Know Where You are, What You might be Doing and We will Catch You: Testing Deterrence Theory in Digital Drug Markets’, The British Journal of Criminology, fifty eight(2): 414-33.

Ladegaard, I. (2019), ‘Crime Displacement in Digital Drug Markets’, International Journal of Drug Policy, 63: 113-21.

Ladegaard, I. (2020), ‘Open Secrecy: How Police Crackdowns and inventive Problem-Solving introduced Illegal Markets out of the Shadows’, Social Forces, 99: 532-559.

Leukfeldt, R. E., Kleemans, E. R., and Stol, W. P. (2017), ‘Cybercriminal Networks, Social Ties and Online Forums: Social Ties Versus Digital Ties inside Phishing and Malware Networks.’, British Journal of Criminology, 57(3): 704-22.

Lusher, D., Koskinen, J., and Robins, G. (2013), Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications, Cambridge University Press.

Martin, J., Munksgaard, R., Coomber, R., Demant, J., and Barratt, M. J. (2020), ‘Selling Drugs on Darkweb Cryptomarkets: Differentiated Pathways, Risks and Rewards’, British Journal of Criminology, 60(three): 559-78.

McGloin, J. M., an