TY  - CONF
A1  - Büsgen, André
A1  - Klöser, Lars
A1  - Kohl, Philipp
A1  - Schmidts, Oliver
A1  - Kraft, Bodo
A1  - Zündorf, Albert
T1  - Exploratory analysis of chat-based black market profiles with natural language processing
T2  - Proceedings of the 11th International Conference on Data Science, Technology and Applications
N2  - 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.
KW  - Clustering
KW  - Natural Language Processing
KW  - Information Extraction
KW  - Profile Extraction
KW  - Text Mining
Y1  - 2022
UR  - https://opus.bibliothek.fh-aachen.de/opus4/frontdoor/index/index/docId/10111
SN  - 978-989-758-583-8
SN  - 2184-285X
SP  - 83
EP  - 94
ER  -