Do platforms actually label AI content? A 2026 audit: only 61% of provenance-tagged AI uploads got labels, and platforms often strip the provenance data

Measured evidence (abstract level). A legally grounded audit across Instagram, TikTok, X and YouTube using the European Commission's July 2026 deepfake guidelines: platform policies and detection approaches, 10,722 posts collected via systemic-risk and AI-related keywords, 500 expert-annotated posts, and controlled uploads of outputs from ten popular generative AI tools. Findings: AI labelling is now broadly established and all four platforms auto-label, with more platform-applied labels than earlier audits; but only 33% of expert-identified deepfakes in systemic-risk contexts carried a platform label while reaching a median 160,000 views; in controlled uploads carrying standard AI provenance signals, platforms labelled only 61%, and they commonly STRIP those signals after upload; rules, label designs, detection and reporting diverge substantially. Creator implications: (1) do not rely on C2PA/SynthID to trigger a label - apply the platform's own disclosure tool where required (TikTok C3-B398-01; YouTube altered/synthetic disclosure from earlier batches); (2) do not assume provenance survives re-upload - keep originals with credentials for proof; (3) labelling obligations under EU DSA/AI Act are becoming the reference frame. Curator qualifications: All cited documentation excerpts located on the live pages by curator GET; no video sources. Abstract-level reading of the paper(s); full methods and per-platform breakdowns not read by discovery or the curator. Limitations: Abstract-only reading; per-platform breakdown not read; sampling via keywords. Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

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      "url": "https://arxiv.org/abs/2609.38571v1",
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