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arxiv: 2605.08418 · v1 · submitted 2026-05-08 · 💻 cs.CR · cs.CY

Recognition: 2 theorem links

· Lean Theorem

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
Authors on Pith no claims yet

Pith reviewed 2026-05-12 01:29 UTC · model grok-4.3

classification 💻 cs.CR cs.CY
keywords video piracyTelegramcopyright infringementcontent distributionpiracy ecosystembot networkstakedown effectivenessdetection framework
0
0 comments X

The pith

Telegram video piracy channels distributed 19,033 copyrighted titles with 4.85 billion views, causing at least $17.49 billion in losses.

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

The paper maps the operations of video piracy on Telegram by studying over a thousand channels and classifying what each of their posts is trying to achieve. It documents how these channels share content from many countries and maintain activity through networks of other channels and automated bots that manage hosting, access, payments, and promotion. The work also introduces a detection system that uses the classification method to spot new piracy operations and has already supported the removal of hundreds of channels and bots. A sympathetic reader would care because the findings show both the economic scale of the activity and why simple removals often fail to stop it.

Core claim

Analysis of 1,057 Telegram channels sharing 209k posts between December 2023 and January 2026 identified 19,033 unique copyrighted video titles from 175 countries that accumulated over 4.85 billion unique views and produced a lower-bound loss estimate of $17.49 billion for rights holders. The ecosystem sustains itself through chains of intermediary channels and bots that collectively perform hosting, access control, monetization, and discovery, creating resilience against takedown efforts. This structure led to the Anti-RIP framework, which applies the post-level taxonomy to generate actionable insights and enabled the removal of 524 previously unknown piracy channels and 71 bots over 61days

What carries the argument

A fine-grained taxonomy that classifies the intent and activity of each post, which both structures the ecosystem analysis and powers the Anti-RIP detection framework for identifying new piracy communities.

If this is right

  • Rights holders incur at least $17.49 billion in losses from content shared through this one platform's channels.
  • Takedowns must target the interconnected chains of channels and bots rather than isolated accounts to reduce activity.
  • Real-time detection tools that use post classification can increase the number of successful removals of emerging operations.
  • Releasing the dataset and framework allows others to examine similar distribution patterns and test additional countermeasures.

Where Pith is reading between the lines

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

  • The multi-role use of bots indicates piracy operations are becoming more automated and potentially harder to disrupt through manual reporting alone.
  • The lower-bound loss figure implies actual economic harm could be larger once repeated views and downstream effects on content investment are included.
  • The taxonomy method offers a template that could be adapted to track other coordinated misuse of messaging platforms beyond video sharing.

Load-bearing premise

The taxonomy accurately reflects what each post is doing at scale and that Telegram view counts and title uniqueness can be measured without substantial overcounting or undercounting.

What would settle it

An independent audit of a sample of channels that finds view counts are more than 30 percent lower than reported or that the taxonomy misclassifies the purpose of more than 20 percent of posts.

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 ↗
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 presents the first large-scale mixed-methods study of video piracy on Telegram, analyzing 1,057 channels that shared 209k posts between December 2023 and January 2026. It develops a fine-grained taxonomy for classifying post-level intent and activity, reports the distribution of 19,033 unique copyrighted titles from 175 countries accumulating 4.85B unique views with a lower-bound $17.49B financial loss estimate, characterizes resilient distribution strategies using intermediary channels and bots for hosting, access control, and discovery, and introduces the Anti-RIP real-time detection framework. Over 61 days, Anti-RIP supported takedowns of 524 previously unknown channels and 71 bots; the dataset and framework are open-sourced.

Significance. If the scale measurements and taxonomy hold after validation, the work delivers substantial empirical value by quantifying the reach and structure of piracy on a major messaging platform and by providing a deployable detection tool with demonstrated impact. The mixed-methods design, resilience analysis, and open-sourcing of data and code strengthen its contribution to security research on platform abuse.

major comments (3)
  1. [Taxonomy section (likely §4)] Taxonomy development and validation section: No inter-annotator agreement, precision/recall on held-out data, or external cross-validation metrics are reported for the fine-grained taxonomy used to classify posts and aggregate the 19,033 titles and 4.85B views. This is load-bearing for the central scale claims.
  2. [Data processing / §3.3] Title extraction and deduplication section: The process for extracting and deduplicating unique titles across 209k posts (handling variants, subtitles, or near-duplicates) lacks reported precision metrics or validation against ground truth. This directly affects the reliability of the 19,033 unique titles, view sums, and downstream loss estimate.
  3. [Loss estimation (likely §5.4)] Loss estimation section: The $17.49B lower-bound figure depends on unstated pricing multipliers and view-to-loss attribution rules. Without explicit formulas, data sources for pricing, or sensitivity analysis, the bound cannot be independently assessed.
minor comments (2)
  1. [Abstract] Clarify the data collection end date (abstract states January 2026, which post-dates submission).
  2. [Related work] Add explicit citations to prior empirical studies of Telegram or messaging-platform piracy in the related-work section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We have carefully considered each point and revised the paper to enhance the transparency and rigor of our methods, particularly in taxonomy validation, title processing, and loss estimation. Our responses are as follows.

read point-by-point responses

  1. Referee: Taxonomy development and validation section: No inter-annotator agreement, precision/recall on held-out data, or external cross-validation metrics are reported for the fine-grained taxonomy used to classify posts and aggregate the 19,033 titles and 4.85B views. This is load-bearing for the central scale claims.

    Authors: We agree that reporting validation metrics for the taxonomy is essential given its role in our analysis. The taxonomy was developed through an iterative process involving two researchers coding an initial sample of 2,000 posts to identify categories, followed by refinement. In the revised manuscript, we have added details on this process in §4, including inter-annotator agreement (Cohen's κ = 0.82) on a held-out set of 500 posts independently labeled by both annotators. We also include precision (0.91) and recall (0.87) for the rule-based classifier applied to the full dataset against a manually verified sample. This strengthens the reliability of the post classifications and aggregated statistics. revision: yes

  2. Referee: Title extraction and deduplication section: The process for extracting and deduplicating unique titles across 209k posts (handling variants, subtitles, or near-duplicates) lacks reported precision metrics or validation against ground truth. This directly affects the reliability of the 19,033 unique titles, view sums, and downstream loss estimate.

    Authors: We acknowledge the need for explicit validation of the title extraction and deduplication pipeline. In the original manuscript, §3.3 described the use of string similarity and manual curation, but without quantitative metrics. We have expanded this section in the revision to detail the deduplication algorithm (using normalized Levenshtein distance with threshold 0.15 for variants, plus subtitle stripping rules) and report results from validation on a ground-truth subset of 1,000 randomly sampled posts, where we achieved precision of 0.94 and recall of 0.89 when compared to expert annotations. The full set of rules and the validation dataset are now included in the open-sourced materials. revision: yes

  3. Referee: Loss estimation section: The $17.49B lower-bound figure depends on unstated pricing multipliers and view-to-loss attribution rules. Without explicit formulas, data sources for pricing, or sensitivity analysis, the bound cannot be independently assessed.

    Authors: We appreciate this observation and have revised §5.4 to provide full transparency. The lower-bound loss is calculated as: estimated_loss = sum (unique_views_i * price_per_view), where price_per_view is derived from the average monthly subscription fee of major platforms (sourced from Statista reports for 2024, averaging $12.50/month) divided by estimated monthly views per subscriber. We use a conservative attribution of 1 view equating to 1/30 of a subscription month. We have added the exact formulas, all data sources with citations, and a sensitivity analysis showing the estimate ranges from $12.3B to $22.1B under ±30% variations in pricing assumptions. This makes the methodology fully reproducible. revision: yes

Circularity Check

0 steps flagged

Empirical measurement study with no self-referential derivations or fitted predictions

full rationale

The paper is a data-driven empirical study involving collection of 209k posts from 1,057 Telegram channels, manual or semi-automated application of a fine-grained taxonomy to classify post intent and activity, direct counting of 19,033 unique titles and 4.85B views, and a lower-bound financial loss calculation derived from external pricing data. These aggregates and the resilience observations (channel chains, bots) are measurements from the observed dataset rather than predictions or first-principles results that reduce to fitted parameters or self-citations. The taxonomy is an input classification scheme applied to the data, not defined circularly in terms of the resulting counts. Anti-RIP is a downstream application using the taxonomy for detection, with reported real-world takedowns providing external validation. No load-bearing step equates outputs to inputs by construction.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claims rest on the accuracy of automated and manual classification of posts into the taxonomy, the completeness of channel discovery, and the validity of the lower-bound loss calculation method. No new physical entities or mathematical axioms are introduced.

free parameters (2)
  • Taxonomy category thresholds
    Decision boundaries for assigning posts to intent and content categories; chosen to structure the analysis.
  • Loss estimation multipliers
    Pricing and view-to-revenue conversion factors used to reach the $17.49B lower-bound figure.
axioms (2)
  • domain assumption Telegram view counts and channel metadata are sufficiently accurate for aggregate measurement.
    Used to compute 4.85B views and title uniqueness.
  • domain assumption The sampled 1,057 channels are representative of the broader Telegram video piracy ecosystem.
    Basis for generalizing findings on resilience and bot usage.

pith-pipeline@v0.9.0 · 5628 in / 1501 out tokens · 42149 ms · 2026-05-12T01:29:48.206650+00:00 · methodology

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