Binge, Bot, Repeat: Unpacking the Ecosystem of Video Piracy on Telegram
Authors: Sadikshya Gyawali, Jaishnoor Kaur, Taylor Graham et al.
Summary
The authors conducted the first large-scale study of video piracy on Telegram, analyzing 1,057 channels that posted 209k posts over two years. They found a massive, deliberately resilient ecosystem: 19,033 unique pirated titles from 175 countries, 4.85 billion views, and an estimated $17.49 billion in losses to rights holders. The system is engineered to resist takedowns through chains of intermediary channels and automated bots that handle hosting, access control, and monetization. The authors built Anti-RIP, a real-time detection framework using a taxonomy of piracy channel behaviors, which helped take down 524 previously unknown channels and 71 bots in 61 days.
Main takeaways:
- Telegram hosts industrial-scale video piracy with billions of views and multi-billion-dollar estimated losses
- The ecosystem uses automated bots and intermediary channels to route around takedowns
- 19,033 unique copyrighted titles from 175 countries were distributed
- Anti-RIP detection framework uses a behavioral taxonomy to identify piracy channels in real-time
- In 61 days, Anti-RIP facilitated takedown of 524 channels and 71 bots
Relevance
No connection to my LLM persona and behavioral-conditioning research — this is content moderation and platform abuse at scale. Not relevant unless I pivot to studying harmful-content distribution through chat platforms, which isn't part of my current work.
Abstract
arXiv:2605.08418v1 Announce Type: new Abstract: Telegram has emerged as a major platform for large-scale video piracy, where copyrighted content is rapidly distributed among users. Despite its prominence, the structural and operational dynamics of this ecosystem remain insufficiently understood. To address this gap, we present the first large-scale study of video piracy on Telegram through a mixed-method analysis of 1,057 channels that shared 209k unique posts between December 2023 and January 2026 - systematically characterizing their content, distribution strategies, and how the ecosystem is sustained at scale. Central to our approach is the development of a fine-grained taxonomy that enables a structured understanding of the activity and intent of these channels on a per-post level. The channels collectively distributed 19,033 unique copyrighted titles originating from 175 countries, accumulating over 4.85B unique views and resulting in a lower-bound estimated financial loss of $17.49B for content rights holders. We also find that this ecosystem is deliberately engineered to be resilient against takedown efforts, frequently redirecting users through chains of intermediary channels and automated bots that collectively handle hosting, access control, monetization, and channel discovery. The scale and persistence of this ecosystem motivated the development of Anti-RIP, a real-time framework for detecting emerging video piracy communities on Telegram. Anti-RIP utilizes our taxonomy to generate contextual, interpretable insights that stakeholders confirmed improve the triaging action against reported posts and channels. Over a 61-day period, the framework facilitated the takedown of 524 previously unknown piracy channels and 71 bots. To support reproducibility and future research, we open-source both the dataset and the Anti-RIP framework.