The goals of this project are to identify the conditions that 1) allow illicit online drug markets to emerge and evolve, 2) enable drug dealers to distribute drugs to customers and 3) incentivize buyers to purchase illegal drugs on the market. In less than ten years, we have witnessed an unprecedented rise and spread of illegal online cryptomarkets, especially those that traffic in illegal drugs, on the "darknet" -- an encrypted region of the Internet only accessible via anonymous 'Tor' browsers. Unlike surface web markets, on the darknet sellers' and buyers' identities are protected through encryption software and the use of cryptocurrency. Such identity protection measures make the darknet particularly appealing to those engaging in illegal behaviors, including trafficking in illegal drugs. This project will advance knowledge in several key domains by integrating insights from criminal, economic, and sociological theories to understand the importance of trust for creating and maintaining social order on illegal drug markets on the darknet, including: a) revealing how trust is signaled, managed, and used by dealers and buyers engaging in illegal drug trade, b) determining whether and how the importance of different trust-signaling strategies enhance dealers' marketability or change over the career of a drug dealer or with buyer experience, c) evaluating the effect of attacks to dealers' reputations as trustworthy trade partners, as well as how dealers resolve such moments of conflict when their trustworthiness is questioned, and d) assessing whether and how trust can be leveraged to disrupt drug trade on the darknet and reduce illegal online drug trade. The current study will provide critical information that law enforcement can leverage when considering strategies that have potential to disrupt the illegal drug trade on the darknet.
To address these goals, longitudinal network and text data will be collected from three popular drug cryptomarkets operating on the darknet covering the time periods of October 2014—January 2020. Each of these cryptomarkets include temporal information on all drug transactions occurring in the market and the network of ties between dealers and buyers engaging in trade as well as textual information from dealers’ landing page, reviewer comments, and discussion board posts. Data will be analyzed by combining qualitative discourse analysis, natural language processing, statistical methods for network analysis including ERGMs and relational events models, growth-curve trajectory models, and agent-based simulation models for assessing the consequences of market disruptions. By leveraging a variety of data available from these cryptomarkets, we can qualitatively identify the processes through which trust emerges, cooperation evolves, and conflicts are resolved, and then quantify these processes through computational text mining. The unprecedented level of detail available in the collected data provides a novel opportunity to monitor online drug market dynamics, as well as the opportunity to observe the real-time consequences of changes in drug policy, the impact of drug epidemics, the emergence of new psychotropic substances, the role of distributed trust for facilitating market activity, and the effectiveness of different law enforcement disruption strategies.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
