cs.CRcs.CY
Binge, Bot, Repeat: Unpacking the Ecosystem of Video Piracy on Telegram
May 8, 2026(revised May 19, 2026)
This study provides the first large-scale analysis of video piracy on Telegram, quantifying its massive financial impact and developing a resilient detection framework, Anti-RIP, to combat it.
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.