Assessing Crime Disclosure Patterns in a Large-Scale Cybercrime Forum

TL;DR

This study analyzes over 3.5 million posts from a major cybercrime forum to understand how users disclose criminal activity, revealing that a significant portion openly or ambiguously share illicit content, with implications for law enforcement detection strategies.

Contribution

It introduces a large-scale assessment of crime disclosure patterns using LLM-based classification and Markov models, providing novel insights into user behavior in cybercrime forums.

Findings

25% of initial posts explicitly mention crimes

Over one third of users disclose crimes at least once

Most users gradually escalate disclosure levels

Abstract

Cybercrime forums play a central role in the cybercrime ecosystem, serving as hubs for the exchange of illicit goods, services, and knowledge. Previous studies have explored the market and social structures of these forums, but less is known about the behavioral dynamics of users, particularly regarding participants' disclosure of criminal activity. This study provides the first large-scale assessment of crime disclosure patterns in a major cybercrime forum, analysing over 3.5 million posts from nearly 300k users. Using a three-level classification scheme (benign, grey, and crime) and a scalable labelling pipeline powered by large language models (LLMs), we measure the level of crime disclosure present in initial posts, analyse how participants switch between levels, and assess how crime disclosure behavior relates to private communications. Our results show that crime disclosure is relatively normative: one quarter of initial posts include explicit crime-related content, and more than one third of users disclose criminal activity at least once in their initial posts. At the same time, most participants show restraint, with over two-thirds posting only benign or grey content and typically escalating disclosure gradually. Grey initial posts are particularly prominent, indicating that many users avoid overt statements and instead anchor their activity in ambiguous content. The study highlights the value of LLM-based text classification and Markov chain modelling for capturing crime disclosure patterns, offering insights for law enforcement efforts aimed at distinguishing benign, grey, and criminal content in cybercrime forums.