• search hit 5 of 49
Back to Result List

From cracked accounts to fake IDs: user profiling on German telegram black market channels

  • Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.

Export metadata

Additional Services

Share in Twitter Search Google Scholar
Metadaten
Author:André BüsgenORCiD, Lars KlöserORCiD, Philipp Kohl, Oliver Schmidts, Bodo Kraft, Albert Zündorf
DOI:https://doi.org/10.1007/978-3-031-37890-4_9
ISBN:978-3-031-37889-8 (Print)
ISBN:978-3-031-37890-4 (Online)
Parent Title (English):Data Management Technologies and Applications
Publisher:Springer
Place of publication:Cham
Editor:Alfredo Cuzzocrea, Oleg Gusikhin, Slimane Hammoudi, Christoph Quix
Document Type:Conference Proceeding
Language:English
Year of Completion:2023
Date of the Publication (Server):2023/07/27
Tag:Clustering; Information extraction; Natural language processing; Profile extraction; Text mining
First Page:176
Last Page:202
Note:
10th International Conference, DATA 2021, Virtual Event, July 6–8, 2021, and 11th International Conference, DATA 2022, Lisbon, Portugal, July 11-13, 2022
Link:https://doi.org/10.1007/978-3-031-37890-4_9
Zugriffsart:campus
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik
collections:Verlag / Springer