Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements
Massimo La Morgia, Alessandro Mei, Alberto Maria Mongardini, Jie Wu
TL;DR
This paper analyzes Telegram's dark side by building TGDataset, a broad snapshot of 35,382 channels and 132,804,557 messages to study verified, scam, clone, and fake channels, as well as the Sabmyk conspiracy network. It introduces a machine learning detector that labels fake channels with an accuracy of 86% and analyzes clones, the graph structure of channel forwarding, and topic distributions via LDA, revealing how information spreads and how malicious actors operate. Key contributions include publicly releasing TGDataset, a fake-channel detector with quantifiable performance, 83 clone channels characterization, and a detailed Sabmyk network analysis showing rapid, coordinated diffusion across multilingual channels. The findings advance understanding of Telegram's ecosystem, inform safer platform use and moderation, and suggest future work on media content, groups, and broader API-enabled analyses.
Abstract
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.