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arxiv: 2605.08418 · v2 · pith:TCQDNSGPnew · submitted 2026-05-08 · 💻 cs.CR · cs.CY

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

Sadikshya Gyawali , Jaishnoor Kaur , Taylor Graham , Josef Horacek , Nowshin Tabassum , Shirin Nilizadeh , Sayak Saha Roy This is my paper

Pith reviewed 2026-05-21 09:26 UTC · model grok-4.3

classification 💻 cs.CR cs.CY
keywords video piracyTelegramcopyright infringementbot networkscontent distributiononline ecosystemsdigital piracytakedown effectiveness
0
0 comments X

The pith

Telegram video piracy relies on chains of intermediary channels and bots to stay resilient against takedowns while distributing thousands of copyrighted titles.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies video piracy on Telegram through analysis of 1,057 channels that shared 209,000 posts containing 19,033 unique copyrighted titles from 175 countries. It documents how the operations use layered networks of helper channels and automated bots to manage user access, monetization, content hosting, and discovery, which allows the activity to continue even when some parts are removed. The study measures the resulting scale, including over 4.85 billion views and an estimated 17.49 billion dollars in losses to rights holders. It also presents a detection approach based on classifying post activities that supported the removal of 524 channels and 71 bots during a two-month window. This provides concrete data on how piracy persists on messaging platforms and one method for identifying new instances.

Core claim

The video piracy ecosystem on Telegram 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 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. The Anti-RIP framework, which applies a fine-grained taxonomy of per-post activity, facilitated the takedown of 524 previously unknown piracy channels and 71 bots over a 61-day real

What carries the argument

The fine-grained taxonomy that classifies per-post activity and intent in piracy channels, which powers detection of new operations and supports the Anti-RIP framework for generating actionable insights on channel networks and bots.

If this is right

  • Individual channel takedowns have limited effect because users are redirected through remaining intermediary channels and bots.
  • The quantified distribution of 19,033 titles across 175 countries and 4.85 billion views establishes a measurable economic impact on rights holders.
  • Taxonomy-based detection improves the ability to identify and act on new piracy communities in real time.
  • Releasing the dataset and framework allows others to replicate the analysis or build on the classification approach for similar platforms.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same layered bot and channel structure could appear in piracy on other messaging services, pointing to a need for comparable studies across apps.
  • Rights holders might adapt the post-classification approach to monitor specific titles proactively rather than waiting for reports.
  • Efforts to reduce piracy would gain from focusing on the monetization and discovery layers rather than only removing final distribution points.
  • Combining the detection method with platform-level data on user redirects could reveal how quickly new chains form after disruptions.

Load-bearing premise

The 1,057 sampled channels and the posts collected between December 2023 and January 2026 are representative of the broader ongoing video piracy activity on Telegram, and the taxonomy accurately captures per-post activity and intent without substantial misclassification or selection bias.

What would settle it

A follow-up measurement that finds the volume of unique copyrighted titles and total views on Telegram piracy channels remains at comparable levels after repeated targeted removals of identified bot chains and intermediary networks.

Figures

Figures reproduced from arXiv: 2605.08418 by Jaishnoor Kaur, Josef Horacek, Nowshin Tabassum, Sadikshya Gyawali, Sayak Saha Roy, Shirin Nilizadeh, Taylor Graham.

Figure 1
Figure 1. Figure 1: (A) A post sharing a link to download a popular US [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (Left) A channel with an alphabetical index, where [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: An example of a Dynamic Content retrieval bot [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: (Top) A channel promotion bot which provides the [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Taxonomy of piracy-related posts on Telegram derived from our insights in Section III. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt used to classify posts using Taxonomy. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of the top 10 production companies whose [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Conceptual illustration of the network structure and [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The Anti-RIP framework Right-holders’ response: Overall, we reported 14,742 piracy posts associated with copyrighted content to the abuse teams of 17 U.S.-based production houses, with reports gen￾erated and emailed on a per-occurrence basis whenever Anti￾RIP identified content tied to a given publisher. For ethical and operational reasons, we do not disclose the identities of participating publishers or … view at source ↗
Figure 11
Figure 11. Figure 11: The Anti-RIP framework Right-holders’ response: Overall, we reported 14,742 piracy posts associated with copyrighted content to the abuse teams of 17 U.S.-based production houses, with reports gen￾erated and emailed on a per-occurrence basis whenever Anti￾RIP identified content tied to a given publisher. For ethical and operational reasons, we do not disclose the identities of participating publishers or … view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper conducts the first large-scale mixed-methods study of video piracy on Telegram by analyzing 1,057 channels that posted 209k unique items between December 2023 and January 2026. It introduces a fine-grained per-post taxonomy to classify activities such as hosting, redirection, monetization, and discovery; reports that these channels distributed 19,033 copyrighted titles from 175 countries, generating 4.85 billion views and an estimated $17.49 billion in lower-bound losses; characterizes the ecosystem as deliberately resilient through chains of intermediary channels and bots; and presents the Anti-RIP detection framework, which stakeholders used to achieve 524 channel and 71 bot takedowns over 61 days. The dataset and framework are released for reproducibility.

Significance. If the sampling and taxonomy claims hold, the work supplies concrete, large-scale evidence on the operational structure of piracy on messaging platforms, quantifies its economic impact, and demonstrates a deployable, taxonomy-driven tool that produced verified real-world takedowns. The open-sourcing of both the 209k-post dataset and the Anti-RIP implementation is a notable strength that enables follow-on measurement and mitigation research in cybersecurity and digital rights.

major comments (3)
  1. [§3 (Data Collection)] §3 (Data Collection): The sampling procedure that produced the 1,057 channels is not described (e.g., keyword search, snowball sampling from known seeds, or random crawl). This directly affects the internal validity of the scale claims (4.85 B views, $17.49 B loss) and the resilience characterization, because any bias toward high-visibility or easily discoverable channels would inflate both the reported volume and the apparent density of redirection chains.
  2. [§4 (Taxonomy and Classification)] §4 (Taxonomy and Classification): No validation of the fine-grained taxonomy is reported (inter-annotator agreement, ground-truth labeling, or error analysis on categories such as “intermediary” vs. “final” channel). Systematic mislabeling of redirection or access-control posts would undermine the central claim that the ecosystem is “deliberately engineered to be resilient” through intermediary chains.
  3. [§5 (Financial Impact)] §5 (Financial Impact): The conversion parameters used to translate views into the $17.49 B loss figure are listed as free parameters but are not justified or sensitivity-tested. Because this number is used to motivate both the scale of the problem and the value of Anti-RIP, the lack of transparent derivation weakens the quantitative claims.
minor comments (2)
  1. [Abstract and §2] The abstract and §2 would benefit from a brief statement of the exact time window (December 2023–January 2026) and the total number of unique posts (209k) to allow readers to assess temporal coverage without consulting later sections.
  2. [Results figures] Figure captions for the redirection-chain diagrams should explicitly state the number of channels and bots in each illustrated example so that the visual evidence can be directly compared to the aggregate statistics.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which has identified important areas for improving the transparency and rigor of our manuscript. We address each major comment point by point below, outlining the specific revisions we will make.

read point-by-point responses

  1. Referee: [§3 (Data Collection)] The sampling procedure that produced the 1,057 channels is not described (e.g., keyword search, snowball sampling from known seeds, or random crawl). This directly affects the internal validity of the scale claims (4.85 B views, $17.49 B loss) and the resilience characterization, because any bias toward high-visibility or easily discoverable channels would inflate both the reported volume and the apparent density of redirection chains.

    Authors: We agree that the sampling procedure requires explicit description to allow proper evaluation of internal validity. The 1,057 channels were identified via an initial set of keyword searches on Telegram for terms associated with video piracy, followed by snowball sampling that traced redirection links and channel recommendations from the seed set, with additional filtering against public lists of known piracy communities. In the revised manuscript we will add a dedicated subsection in §3 that fully documents this multi-stage process, discusses its limitations including potential over-representation of high-visibility channels, and explains how these factors should inform interpretation of the reported scale and resilience findings. revision: yes

  2. Referee: [§4 (Taxonomy and Classification)] No validation of the fine-grained taxonomy is reported (inter-annotator agreement, ground-truth labeling, or error analysis on categories such as “intermediary” vs. “final” channel). Systematic mislabeling of redirection or access-control posts would undermine the central claim that the ecosystem is “deliberately engineered to be resilient” through intermediary chains.

    Authors: We acknowledge that the absence of reported validation metrics leaves the taxonomy's reliability open to question and could weaken the resilience characterization. The taxonomy was developed iteratively through team discussions informed by observed post patterns and consultation with domain experts; however, formal inter-annotator agreement and error analysis were not included in the original submission. In the revision we will add these elements to §4, reporting agreement statistics on a labeled sample and a focused error analysis of the intermediary versus final channel distinction, thereby providing stronger support for the claim that the ecosystem is deliberately engineered for resilience. revision: yes

  3. Referee: [§5 (Financial Impact)] The conversion parameters used to translate views into the $17.49 B loss figure are listed as free parameters but are not justified or sensitivity-tested. Because this number is used to motivate both the scale of the problem and the value of Anti-RIP, the lack of transparent derivation weakens the quantitative claims.

    Authors: We thank the referee for noting the need for greater transparency in the financial estimates. The parameters were drawn from industry reports on per-view revenue and piracy loss multipliers, but these sources and any sensitivity considerations were not detailed. In the revised §5 we will provide explicit citations and justifications for each parameter and include a sensitivity analysis that varies the key conversion factors across plausible ranges, demonstrating that the lower-bound loss estimate remains substantial even under conservative assumptions. This will strengthen the motivation for both the scale of the problem and the utility of Anti-RIP. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical observational study

full rationale

The paper reports an empirical mixed-methods study grounded in direct collection of 1,057 channels and 209k posts, followed by manual taxonomy development and application to characterize distribution patterns and build the Anti-RIP detector. No equations, first-principles derivations, fitted parameters, or predictions appear; the resilience claim and takedown counts are presented as observed outcomes from the sampled data and framework deployment rather than quantities that reduce to the authors' own definitions or inputs by construction. The work is self-contained against external benchmarks of collected posts and reported platform actions.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claims rest on the representativeness of the sampled channels, the validity of the taxonomy for inferring intent, and the accuracy of view and loss estimates; the abstract supplies no details on data collection pipeline, inter-rater reliability for the taxonomy, or the exact formula used for the $17.49B lower-bound loss figure.

free parameters (1)
  • Financial loss conversion parameters
    The $17.49B lower-bound loss estimate necessarily depends on assumptions about revenue per view or equivalent subscription value that are not specified.
axioms (1)
  • domain assumption The 1,057 channels collected are representative of Telegram video piracy activity during the study window.
    Generalization from the observed sample to the full ecosystem depends on this assumption.

pith-pipeline@v0.9.0 · 5859 in / 1456 out tokens · 50348 ms · 2026-05-21T09:26:40.431984+00:00 · methodology

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    Relation between the paper passage and the cited Recognition theorem.

    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 scale and persistence of this ecosystem motivated the development of Anti-RIP, a real-time framework for detecting emerging video piracy communities on Telegram.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    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...

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