The Network Structure of Opioid Distribution on a Darknet Cryptomarket

Abstract

Objectives

The current study is the first to examine the network structure of an encrypted online drug distribution network. It examines (1) the global network structure, (2) the local network structure, and (3) identifies those vendor characteristics that best explain variation in the network structure. In doing so, it evaluates the role of trust in online drug markets.

Methods

The study draws on a unique dataset of transaction level data from an encrypted online drug market. Structural measures and community detection analysis are used to characterize and investigate the network structure. Exponential random graph modeling is used to evaluate which vendor characteristics explain variation in purchasing patterns.

Results

Vendors’ trustworthiness explains more variation in the overall network structure than the affordability of vendor products or the diversity of vendor product listings. This results in a highly localized network structure with a few key vendors accounting for most transactions.

Conclusions

The results indicate that vendors’ trustworthiness is a better predictor of vendor selection than product diversity or affordability. These results illuminate the internal market dynamics that sustain digital drug markets and highlight the importance of examining how new anonymizing technologies shape global drug distribution networks.

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Fig. 1
Fig. 2

Notes

  1. 1.

    Colloquially, anonymous activity on Tor and other encrypted networks are often referred to as activities on the ‘darknet,’ although this is not technically correct.

  2. 2.

    Most literature in this field refers to Tor markets as ‘cryptomarkets.’ We use the term Tor marketplace to avoid confusion with the specific marketplace we examine, Cryptomarket.

  3. 3.

    Decary-Hetu et al. (2016) note that risk is greater in international distribution because sanctions are harsher than domestic distribution and because contraband crossing national boarders is more likely to be seized than contraband traveling within national borders.

  4. 4.

    Cryptomarket was also selected for data collection because it is the only large Tor market that provides the entire username for both vendors and buyers, allowing network ties to be identified. Most other Tor drug markets encrypt usernames to preserve anonymity. The ability to match identifiers is necessary to reconstruct the network.

  5. 5.

    Schedule 1 is the highest degree of control in the US. It is reserved for substances with high abuse potential and no approved medical usage.

  6. 6.

    Many vendors miscategorize their listings purposely to advertise to users who typically use different drugs. We classified each drug according to their chemical category, rather than how they were listed on Cryptomarket.

  7. 7.

    Reasons for poor transactions include long shipment times, products not being delivered as described, poor communication between vendors and sellers, and non-delivery of items.

  8. 8.

    Unfortunately, the country of origin for buyers is not listed on Cryptomarket.

  9. 9.

    It is important to clarify here that all vendors were still active, even though the listing may not be. For example, when the webpages were downloaded, the vendor may no longer sell a particular drug, even though the vendor had sold that drug at some point in the prior 6 months.

  10. 10.

    This did not result in any missing ties in the network data. Missing ties are the source of most missing-data based estimation problems in network analysis (Robins et al. 2004; Wang et al. 2016). Missing tie values were present for 37% of ties, but did not impact ERGM results when we reran the models without the affordability variable—the only variable based on tie values.

  11. 11.

    An actor’s degree score and degree centrality are synonymous.

  12. 12.

    To make interpretation easier, we added ‘6’ to every vendor’s reputation score so that 0 becomes the lowest score possible, indicating vendors who have not made a sale in the past 6 months. We compared this decision to z-score and logarithmic transformations of the reputation score to verify the robustness of our results.

  13. 13.

    We also ran the ERGM with an average evaluation per sale measure for vendors, finding the same pattern as that presented below. Because the ERGM controls for the number of sales made to each vendor, we used the cumulative reputation score in our models.

  14. 14.

    We reran the model measuring vendors’ affordability as the average price per gram of drug for a vendor (e.g. average of $40 for a 1 g drug transaction), encountering the same results: vendors’ reputation was positively correlated (p < 0.001) with making a sale and affordability was non-significant. We elected to use the more parsimonious measure to restrict missing data in the model.

  15. 15.

    It is important to note here that only vendors can be isolates, as buyers could only enter the network after a connection (transaction) has been made. Thus, there are 57 vendors total, but only 44 vendors who have made a sale in the last 6 months.

  16. 16.

    This may be due to vendors using different accounts to purchase. However, we have found no source, scholarly or otherwise, to corroborate this.

  17. 17.

    Technically, bipartite networks are not directed. However, we continue to use the language of indegree/outdegree for the sake of consistency.

  18. 18.

    All network statistics provided are calculated by treating the network as bipartite, with the exception of transitivity, which cannot exist in a bipartite network.

  19. 19.

    We reran this model without the mean transaction variable—the only variable influenced by missing data—with little difference in statistical significance, standard errors, and coefficient size.

  20. 20.

    Prior research has highlighted that many vendors list their geographic location as ‘worldwide’ to avoid identification. Eight of our 57 vendors were listed as worldwide. This information was not included in our vendors’ location variable. As one reviewer pointed out, there may be self-selection among vendors who do not list their country of origin.

  21. 21.

    While unintuitive given our descriptive findings, this merely reflects that the only buyer level variable in our model is their degree score, which tends to be significant if no other controls are included in the model (Lusher and Ackland 2011), and that all buyers in our network have a degree score >1.

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Acknowledgements

We would like to thank Benjamin Gilbert for assistance in data coding, and David Melamed for helpful methodological suggestions.

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Corresponding author

Correspondence to Scott W. Duxbury.

Appendix

Appendix

In line with Hunter et al. (2008), we evaluate the goodness of fit of ERGM by comparing a distribution of degree statistics from networks simulated from ERGM parameters to the degree statistics of the empirical network. Figure 3a indicates how well the simulated networks match the degree score of the empirical network. Figure 3b indicates how well ERGM coefficients predict the degree scores observed in the empirical network.

Fig. 3
figure3

a Goodness of fit for ERGM, degree. b Goodness of fit for ERGM, log-odds of degree

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Duxbury, S.W., Haynie, D.L. The Network Structure of Opioid Distribution on a Darknet Cryptomarket. J Quant Criminol 34, 921–941 (2018). https://doi.org/10.1007/s10940-017-9359-4

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Keywords

  • Drug distribution
  • Online drug markets
  • Social networks
  • Trust
  • Tor network