Pith. sign in

REVIEW 3 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2111.13530 v3 pith:D7AQ4664 submitted 2021-11-26 cs.CY cs.LGcs.SI

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

classification cs.CY cs.LGcs.SI
keywords channelsfakestelegramclonesidentifyservicesactivitiesconspiracy
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original 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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Binge, Bot, Repeat: Unpacking the Ecosystem of Video Piracy on Telegram

    cs.CR 2026-05 conditional novelty 7.0

    First large-scale mixed-methods analysis of Telegram video piracy reveals a resilient bot-and-channel ecosystem distributing thousands of titles with billions in estimated losses and introduces an open-source detectio...

  2. Binge, Bot, Repeat: Unpacking the Ecosystem of Video Piracy on Telegram

    cs.CR 2026-05 conditional novelty 7.0

    The study maps the resilient video piracy ecosystem on Telegram via a new per-post taxonomy and deploys Anti-RIP, a detection framework that enabled removal of 524 unknown channels and 71 bots.

  3. Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities

    cs.CL 2026-07 conditional novelty 5.0

    Interpreting harmful Discord messages requires integrating external knowledge and extended context, not just local message-level classification; LLMs leverage local context better than humans but still fail on coded l...