The Network Structure of Opioid Distribution on a Darknet Cryptomarket
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.
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.
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.
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.
KeywordsDrug distribution Online drug markets Social networks Trust Tor network
We would like to thank Benjamin Gilbert for assistance in data coding, and David Melamed for helpful methodological suggestions.
- Aldridge J, Décary-Hétu D (2014) Not an ‘Ebay for Drugs’: The cryptomarket ‘Silk Road’ as a paradigm shifting criminal innovation. doi:10.2139/ssrn.2436643
- Decary-Hetu D, Laferriere D (2015) Discrediting vendors in online criminal markets. In: Maim Ali, Bichler Gisela (eds) Disrupting criminal networks: network analysis in crime prevention. Lynne Rienner, BoulderGoogle Scholar
- Eurobarometer (2014) Young people and drugs: Results per country. Retrieved from http://ec.europa.eu/public_opinion/flash/fl_401_en.pdf
- Kennedy D (2008) Deterrence and crime prevention: reconsidering the prospect of sanction. Routledge, LondonGoogle Scholar
- Krebs V (2001) Mapping networks of terrorist cells. Connections 24(3):43–52Google Scholar
- Lusher D, Ackland R (2011). A relational hyperlink analysis of an online social movement. J Soc Struct 12(5):1–35Google Scholar
- Lusher D, Koskinen J, Robins G (2013) Exponential random graph models for social networks: theory, methods, and applications. Cambridge Press, CambridgeGoogle Scholar
- Pons P, Latapy M (2005) Computing communities in large networks using random walks. In: Physics and society. arXiv:physics/0512106v1
- Snijders TA (2002) Markov chain Monte Carlo estimation of exponential random graph models. J Soc Struct 3(2):1–40Google Scholar
- Soska K, Christin N (2015). Measuring the longitudinal evolution of the online anonymous marketplace ecosystem. In: Proceedings of the 24th Usenix security symposiumGoogle Scholar
- Tremblay P (1993) Searching for suitable co-offenders. In: Clarke RV, Felson M (eds) Routine activity and rational choice. Transaction Books, New Brunswick, pp 17–36Google Scholar
- United Nations Office on Drugs and Crime (2016) World drug report. https://www.unodc.org/doc/wdr2016/WORLD_DRUG_REPORT_2016_web.pdf