Investigating and Comparing Discussion Topics in Multilingual Underground Forums

2026-03-23

AI Summary

This study addresses the fragmentation of cybercriminal knowledge caused by linguistic barriers in multilingual underground forums, which hinders comprehensive threat intelligence and risk assessment. For the first time, an unsupervised semantic clustering approach—integrating multilingual topic modeling and contextual semantic analysis—is employed to systematically uncover disparities in knowledge distribution between Russian- and English-speaking segments of dual-language criminal forums. The research identifies covert knowledge subcommunities accessible exclusively to Russian speakers and achieves precise semantic interpretation of dark web jargon. By revealing how language isolation shapes information silos in illicit online ecosystems, this work establishes a novel paradigm for cross-lingual cybercrime intelligence extraction and enhances global situational awareness of emerging threats.

Technology Category

• Data Mining & Knowledge Management: Semantic Web
• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Underground forums play a crucial role in the criminal ecosystem, facilitating the exchange of knowledge and the trade of illegal tools and services. By analyzing the skills, motivations, focus, and operations of cyber-criminals active in these forums, cybersecurity professionals and law enforcement can better understand their tactics, assess the risks they pose to society, and develop more effective countermeasures. A significant challenge in analyzing these forums arises from language barriers, either because they blend different languages or because they use community-specific slang. In this paper, we address this challenge through the use of a combination of unsupervised methods that group together semantically related conversational themes (i.e., topics) into clusters. We apply our methodology to analyze a prolific, invite-only, Russian-English criminal forum that has been operating for over 18 years. This way, we uncover pockets of knowledge, i.e., knowledge only shared in one sub-community. This knowledge is accessible only to those speaking a language (e.g., Russian), thereby showing that language barriers (e.g., for users that do not speak Russian) can create sub-communities with different knowledge and motivations. We further demonstrate how our method can identify the semantic meaning of dark jargon from its context, and discuss other potential applications of our approach.

Problem

• underground forums
• multilingual analysis
• language barriers
• cybercrime
• dark jargon

Innovation

• unsupervised topic clustering
• multilingual underground forums
• dark jargon semantics
• language barriers in cybercrime
• knowledge sub-communities