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.
Verfasserangaben: | André BüsgenORCiD, Lars KlöserORCiD, Philipp Kohl, Oliver Schmidts, Bodo Kraft, Albert Zündorf |
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DOI: | https://doi.org/10.5220/0011271400003269 |
ISBN: | 978-989-758-583-8 |
ISSN: | 2184-285X |
Titel des übergeordneten Werkes (Englisch): | Proceedings of the 11th International Conference on Data Science, Technology and Applications |
Dokumentart: | Konferenzveröffentlichung |
Sprache: | Englisch |
Erscheinungsjahr: | 2022 |
Datum der Publikation (Server): | 29.07.2022 |
Freies Schlagwort / Tag: | Clustering; Information Extraction; Natural Language Processing; Profile Extraction; Text Mining |
Erste Seite: | 83 |
Letzte Seite: | 94 |
Bemerkung: | 11th International Conference on Data Science, Technology and Applications DATA - Volume 1, 83-94, 2022, Lisbon, Portugal |
Link: | https://doi.org/10.5220/0011271400003269 |
Zugriffsart: | weltweit |
Fachbereiche und Einrichtungen: | FH Aachen / Fachbereich Medizintechnik und Technomathematik |
open_access (DINI-Set): | open_access |
Lizenz (Deutsch): | ![]() |