Exploratory analysis of chat-based black market profiles with natural language processing

  • Messenger apps like WhatsApp or Telegram are an integral part of daily communication. Besides the various positive effects, those services extend the operating range of criminals. Open trading groups with many thousand participants emerged on Telegram. Law enforcement agencies monitor suspicious users in such chat rooms. This research shows that text analysis, based on natural language processing, facilitates this through a meaningful domain overview and detailed investigations. We crawled a corpus from such self-proclaimed black markets and annotated five attribute types products, money, payment methods, user names, and locations. Based on each message a user sends, we extract and group these attributes to build profiles. Then, we build features to cluster the profiles. Pretrained word vectors yield better unsupervised clustering results than current state-of-the-art transformer models. The result is a semantically meaningful high-level overview of the user landscape of black market chatrooms. Additionally, the extracted structured information serves as a foundation for further data exploration, for example, the most active users or preferred payment methods.

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Author:Andre BüsgenORCiD, Lars Klöser, Philipp Kohl, Oliver Schmidts, Bodo Kraft, Albert Zündorf
Parent Title (English):Proceedings of the 11th International Conference on Data Science, Technology and Applications
Document Type:Conference Proceeding
Year of Completion:2022
Date of the Publication (Server):2022/07/29
Tag:Clustering; Information Extraction; Natural Language Processing; Profile Extraction; Text Mining
First Page:83
Last Page:94
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell-Keine Bearbeitung