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From Cracked Accounts to Fake IDs: User Profiling on German Telegram Black Market Channels

  • Conference paper
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Data Management Technologies and Applications (DATA 2022, DATA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1860))

  • 228 Accesses

Abstract

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.

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Notes

  1. 1.

    https://github.com/Abuesgen/From-Cracked-Accounts-to-Fake-IDs.git.

  2. 2.

    Google request for the keyphrase “telegram groups” gives many results for search engines.

  3. 3.

    https://core.telegram.org/api/mentions.

  4. 4.

    https://core.telegram.org/.

  5. 5.

    The lower boundary of the second-best score.

  6. 6.

    https://mlco2.github.io/impact#compute.

  7. 7.

    Following Krippendorff’s alpha.

  8. 8.

    At the time of writing this paper, the monthly premium package prices are 5.29 € for NordVPN and 30,00 € for the first year, and 66,90 € afterward for Sky in Germany.

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Author information

Authors and Affiliations

  1. Aachen University of Applied Sciences, 52066, Aachen, Germany

    André Büsgen, Lars Klöser, Philipp Kohl, Oliver Schmidts & Bodo Kraft

  2. University of Kassel, 34109, Kassel, Germany

    Albert Zündorf

Authors
  1. André Büsgen
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  2. Lars Klöser
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  3. Philipp Kohl
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  4. Oliver Schmidts
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  6. Albert Zündorf
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Corresponding authors

Correspondence to André Büsgen , Lars Klöser , Philipp Kohl , Oliver Schmidts , Bodo Kraft or Albert Zündorf .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  2. Ford Motor Company, Commerce Township, MI, USA

    Oleg Gusikhin

  3. Siège du Groupe ESEO, Angers, France

    Slimane Hammoudi

  4. Hochschule Niederrhein, Krefeld, Nordrhein-Westfalen, Germany

    Christoph Quix

Appendix

Appendix

Fig. 12.
figure 12

Affinity difference matrix between all product categories using the fine-tuned FastText embeddings. Equation 1 describes the computation yielding this matrix.

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Fig. 13.
figure 13

We generate an HTML site for the aggregated message information. The translated excerpt shows general information about the user and an example product with prices and counts. If vendors offer the same product in several messages, we aggregate all these messages by the product. The interested viewer can inspect each message from which the NLP approach extracted information. We changed the visualization slightly for the compressed presentation.

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Büsgen, A., Klöser, L., Kohl, P., Schmidts, O., Kraft, B., Zündorf, A. (2023). From Cracked Accounts to Fake IDs: User Profiling on German Telegram Black Market Channels. In: Cuzzocrea, A., Gusikhin, O., Hammoudi, S., Quix, C. (eds) Data Management Technologies and Applications. DATA DATA 2022 2021. Communications in Computer and Information Science, vol 1860. Springer, Cham. https://doi.org/10.1007/978-3-031-37890-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-37890-4_9

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