# Assessing Crime Disclosure Patterns in a Large-Scale Cybercrime Forum

arXiv: https://arxiv.org/abs/2603.01624 · HTML: https://mlsanity.com/paper/2603.01624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 8 of 8)

- [Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of Snapchat](https://mlsanity.com/paper/1906.00355/) (2019)
- [The Use of Large Language Models (LLM) for Cyber Threat Intelligence (CTI) in Cybercrime Forums](https://mlsanity.com/paper/2408.03354/) (2024)
- [DarkGram: A Large-Scale Analysis of Cybercriminal Activity Channels on Telegram](https://mlsanity.com/paper/2409.14596/) (2024)
- [Under the Underground: Predicting Private Interactions in Underground Forums](https://mlsanity.com/paper/1805.04494/) (2018)
- [You Can Tell a Cybercriminal by the Company they Keep: A Framework to Infer the Relevance of Underground Communities to the Threat Landscape](https://mlsanity.com/paper/2306.05898/) (2023)
- [Early warning signals for predicting cryptomarket vendor success using dark net forum networks](https://mlsanity.com/paper/2306.16568/) (2023)
- [Inferring Discussion Topics about Exploitation of Vulnerabilities from Underground Hacking Forums](https://mlsanity.com/paper/2405.04561/) (2024)
- [Identifying Key Expert Actors in Cybercrime Forums Based on their Technical Expertise](https://mlsanity.com/paper/2506.01848/) (2025)

## Abstract (arXiv)

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.