Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law

arXiv:2609.38571v1 Announce Type: new Abstract: AI labels are emerging as a primary safeguard for transparency about AI-generated content on social media, including under the EU Digital Services Act and AI Act. Yet, limited systematic evidence exists on how platforms implement such labels in practice. We conduct a legally grounded audit of AI labelling across Instagram, TikTok, X, and YouTube, drawing on the European Commission's July 2026 guidelines on deepfakes. To this end, we analyse platform policies and detection approaches, 10,722 posts collected via systemic-risk and AI-related keywords, an expert-annotated subset of 500 posts, and controlled uploads to the four platforms of outputs from ten popular generative AI tools. We find that AI labelling is now broadly established. All four platforms apply labels automatically and a greater share of labels are platform-applied than in earlier audits. However, coverage remains incomplete where labelling arguably matters most: only 33% of expert-identified deepfakes in systemic risk contexts carried a platform-applied AI label, while reaching a median of 160,000 views. In controlled uploads of AI-generated content carrying standard AI provenance signals, platforms labelled only 61% of uploads, and commonly strip those signals after uploading. Overall, platform rules, label designs, detection approaches, and reporting diverge substantially. In response, we identify concrete opportunities to improve the uptake, clarity and efficacy of AI labelling.

Incomplete AI labelling on social media platforms poses risks to transparency and trust.

The study finds that AI labelling on social media platforms is incomplete and inconsistent. This lack of transparency can mislead users and pose risks, especially in systemic risk contexts.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   1eef993b-cb4a-401c-bcca-e7c906ebaf79
           https://arxiv.org/abs/2609.38571
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EVIDENCE   3 extracted excerpts
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MODEL RUN  3 runs, methodology 1.0.1
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SCORE      -68  (Strongly adverse)
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CONFIDENCE 82%