Pornhub, payment processors and child sexual abuse material: moral and algorithmic authorities in platform governance
Bibliographic record
Abstract
This article analyses cultural, commercial and technical forces shaping content moderation on pornography platforms. Antipornography frameworks have unjustly cast adult sites as leading perpetrators circulating harmful and sexually abusive content. This framing encourages credit card companies and payment processors to treat porn platforms as high-risk merchants subject to stricter standards and oversight. To mitigate risk, these business partners frequently require use of algorithmic tools for content management. Governance by these private financial firms thus shapes porn platform moderation through both moral panic and automation. To make this dynamic legible, I examine Visa and Mastercard's demonetization of Pornhub in 2020 following a child sexual abuse material (CSAM) scandal. This case captures the range of anti-porn values, influential business partners and automated technologies shaping Pornhub moderation. I first introduce the controversy, detail ‘high-risk' terms for adult merchants and show how rules are interpreted and enforced by payment processors. Next, I review content moderation protocols on Pornhub, considering effects of these tools on porn and the stakes of this governing arrangement. I conclude arguing that interventions on porn platforms framed in service of public safety primarily serve private commercial interests – mitigating ‘reputational risk’ while entrenching other harms in content management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".