Assessing Crime Disclosure Patterns in a Large-Scale Cybercrime Forum
Raphael Hoheisel, Tom Meurs, Jai Wientjes, Marianne Junger, Abhishta Abhishta, Masarah Paquet-Clouston

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…
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Taxonomy
TopicsCybercrime and Law Enforcement Studies · Authorship Attribution and Profiling · Crime Patterns and Interventions
