An academic analysis released in May 2026 illustrates how the Telegram social media platform 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.
The analysis team developed a fine-grained taxonomy that enabled a structured understanding of the activity and intent of these channels on a per-post level and ran a mixed-method analysis of 1,057 Telegram channels — systematically characterizing their content, distribution strategies, and how that ecosystem was sustained at scale
These channels shared 209k unique posts between December 2023 and January 2026 and 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.
The analysts also found that this ecosystem was 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.
Mitigation strategy
The scale and persistence of this ecosystem motivated the analysts to develop Anti-RIP, a real-time framework for detect- ing 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.
[ Note: this content was taken from the paper’s abstract ]
Why it matters
The analysts developed the first large-scale analysis of video piracy on Telegram through a mixed-method study. Insights from this analysis then inform the development of their taxonomy, which automates the inference of post behavior, and provides a structured framework for understanding channel activity.
The taxonomy was then used to quantitatively analyze a much larger selection of posts shared across 983 unique channels. This analysis in turn enabled the analysts to generalize their qualitative insights at scale, identify the volume of copyrighted content, estimate the financial damage across global regions, and map how channels and bots interact among themselves to make content delivery seamless as well as resilient to takedown.
Their findings then guided the development of a real-time, open-source framework for identifying and reporting emerging piracy communities on the platform. That framework produces actionable, evidence-backed reports that significantly reduce the effort required for stakeholders to verify and respond to piracy activity.
The Anti-RIP framework and the associated dataset at https://github.com/Scalable-Security- Research-Lab/BingeBotRepeat, to support further research and enable practical intervention efforts
Further reading
