Contribution to the harm assessment of darknet markets: topic modelling drug reviews on Dark0de Reborn

• Published in: Crime science Vol. 13; no. 1; pp. 13 - 10
• Main Authors: Szigeti, Ákos, Frank, Richard, Kiss, Tibor
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V; BMC
• Subjects: Community and Environmental Psychology; Cooperation; Criminology; Criminology and Criminal Justice; Customers; Darknet markets; Detecting; Digital currencies; Drug overdose; Drug trade; Drug trafficking; Feedback; Harm reduction; Law and Criminolgy; Law enforcement; Measuring; Narcotics; Pandemics; Preventing Cyber Social Threats; Reputations; Signal,Image and Speech Processing; Systems and Data Security; Topic modelling; Trust; Hungary; Darknet markets; Topic modelling; Drug trade
• ISSN: 2193-7680; 2193-7680
• DOI: 10.1186/s40163-024-00211-z

Amid the global opioid crisis, the volume of drug trade via darknet markets has risen to an all-time high. The steady increase can be explained by the reliable operation of darknet markets, affected by community-building trust factors reducing the risks during the process of the darknet drug trade. This study was designed to explore the risk reduction efforts of the community of a selected darknet market and therefore contribute to the harm assessment of darknet markets. We performed Latent Dirichlet Allocation topic modelling on customer reviews of drug products ( n  = 25,107) scraped from the darknet market Dark0de Reborn in 2021. We obtained a model resulting in 4 topics (coherence score = 0.57): (1) feedback on satisfaction with the transaction; (2) report on order not received; (3) information on the quality of the product; and (4) feedback on vendor reliability. These topics identified in the customer reviews suggest that the community of the selected darknet market implemented a safer form of drug supply, reducing risks at the payment and delivery stages and the potential harms of drug use. However, the pitfalls of this form of community-initiated safer supply support the need for universally available and professional harm reduction and drug checking services. These findings, and our methodological remarks on applying text mining, can enhance future research to further examine risk and harm reduction efforts across darknet markets.