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

arXiv: https://arxiv.org/abs/2111.13530 · HTML: https://mlsanity.com/paper/2111.13530/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [The Anatomy of a Cryptocurrency Pump-and-Dump Scheme](https://mlsanity.com/paper/1811.10109/) (2018)
- [On the Globalization of the QAnon Conspiracy Theory Through Telegram](https://mlsanity.com/paper/2105.13020/) (2021)
- [The Pushshift Telegram Dataset](https://mlsanity.com/paper/2001.08438/) (2020)
- [Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations](https://mlsanity.com/paper/2005.06610/) (2020)
- [Dynamical patterns in individual trajectories toward extremism](https://mlsanity.com/paper/1706.01594/) (2017)

## Built on by (most cited first, 5 of 5)

- [Antisemitic Messages? A Guide to High-Quality Annotation and a Labeled Dataset of Tweets](https://mlsanity.com/paper/2304.14599/) (2023)
- [Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram](https://mlsanity.com/paper/2301.10856/) (2023)
- [Application of AI-based Models for Online Fraud Detection and Analysis](https://mlsanity.com/paper/2409.19022/) (2024)
- [Attention Paper: How Generative AI Reshapes Digital Shadow Industry?](https://mlsanity.com/paper/2305.18346/) (2023)
- [Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities](https://mlsanity.com/paper/2607.07277/) (2026)

## 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.