TeleHunt: A Framework and Tool for Efficient Cybercriminal Community Discovery on Telegram

Computer Science > Cryptography and Security [Submitted on 3 Jun 2026] TeleHunt: A Framework and Tool for Efficient Cybercriminal Community Discovery on Telegram Roy Ricaldi, Victor Asanache, Luca Allodi This paper presents TeleHunt, a framework and tool for evaluating the effectiveness of different strategies to discover cybercriminal communities on Telegram. TeleHunt employs a set of reference-driven snowballing strategies, integrating message-level classification, contextual filtering, and market-segment labeling. Using open- and dark-web seeds, we systematically evaluate how seed source, pointer type, and exploration strategy influence discovery outcomes in three dimensions: efficiency, accessibility, and rediscovery. Our work provides (i) a modular cybercrime content discovery pipeline, (ii) the first systematic comparison of Telegram discovery strategies with an empirical characterization of market-segment accessibility, and (iii) a labeled dataset of over 172 million messages from 6,022 Telegram communities. Comments: Accepted for publication in The 21st International Conference on Availability, Reliability and Security (ARES 2026) International Workshop on Cyber Crime in Linkoping, Sweden Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.04657 [cs.CR]   (or arXiv:2606.04657v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.04657 Focus to learn more Submission history From: Roy Ricaldi [view email] [v1] Wed, 3 Jun 2026 09:31:58 UTC (1,599 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)