Early warning signals for predicting cryptomarket vendor success using dark net forum networks

TL;DR

This study develops a network-based method to identify and predict successful vendors in dark net cryptomarkets by analyzing user communication patterns, providing an early warning system for law enforcement.

Contribution

It introduces a novel approach using evolving communication networks and betweenness centrality to predict vendor success before it occurs.

Findings

User activity correlates with vendor success.

High betweenness centrality predicts successful vendors.

Betweenness scores rise before vendor success.

Abstract

In this work we focus on identifying key players in dark net cryptomarkets that facilitate online trade of illegal goods. Law enforcement aims to disrupt criminal activity conducted through these markets by targeting key players vital to the market's existence and success. We particularly focus on detecting successful vendors responsible for the majority of illegal trade. Our methodology aims to uncover whether the task of key player identification should center around plainly measuring user and forum activity, or that it requires leveraging specific patterns of user communication. We focus on a large-scale dataset from the Evolution cryptomarket, which we model as an evolving communication network. Results indicate that user and forum activity, measured through topic engagement, is best able to identify successful vendors. Interestingly, considering users with higher betweenness centrality in the communication network further improves performance, also identifying successful vendors with moderate activity on the forum. But more importantly, analyzing the forum data over time, we find evidence that attaining a high betweenness score comes before vendor success. This suggests that the proposed network-driven approach of modelling user communication might prove useful as an early warning signal for key player identification.