mlsanityData to 7 Oct 2026

Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements

arXiv 2111.13530 ↗First posted 26 Nov 2021cs.CY cs.LG cs.SIcited by 5

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Abstract (arXiv)

Telegram is one of the most used instant messaging apps worldwide. Some of its success lies in providing high privacy protection and social network features like the channels -- virtual rooms in which only the admins can post and broadcast messages to all its subscribers. However, these same features contributed to the emergence of borderline activities and, as is common with Online Social Networks, the heavy presence of fake accounts. Telegram started to address these issues by introducing the verified and scam marks for the channels. Unfortunately, the problem is far from being solved. In this work, we perform a large-scale analysis of Telegram by collecting 35,382 different channels and over 130,000,000 messages. We study the channels that Telegram marks as verified or scam, highlighting analogies and differences. Then, we move to the unmarked channels. Here, we find some of the infamous activities also present on privacy-preserving services of the Dark Web, such as carding, sharing of illegal adult and copyright protected content. In addition, we identify and analyze two other types of channels: the clones and the fakes. Clones are channels that publish the exact content of another channel to gain subscribers and promote services. Instead, fakes are channels that attempt to impersonate celebrities or well-known services. Fakes are hard to identify even by the most advanced users. To detect the fake channels automatically, we propose a machine learning model that is able to identify them with an accuracy of 86%. Lastly, we study Sabmyk, a conspiracy theory that exploited fakes and clones to spread quickly on the platform reaching over 1,000,000 users.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 7 of its 42 references that are in the map. First posted on arXiv 26 Nov 2021.

Builds on

7 of 42 references.

References · 7

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  2. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 379
  3. 2018The Anatomy of a Cryptocurrency Pump-and-Dump Schemecited by 15
  4. 2021On the Globalization of the QAnon Conspiracy Theory Through Telegramcited by 14
  5. 2020The Pushshift Telegram Datasetcited by 12
  6. 2020Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulationscited by 7
  7. 2017Dynamical patterns in individual trajectories toward extremismcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 0; 2026: 1.

5 papers.