Social media platforms inconsistently label ai and deepfake content under eu law
Drowning in AI Slop: How Social Media Platforms (Do Not) Label AI and Deepfake Content under EU law
Computers and Society
Summary
Social media sites like Instagram, TikTok, X, and YouTube are supposed to label content created by artificial intelligence (AI) and deepfakes so users know what is real. The authors studied how well these platforms follow EU laws on labeling AI content by analyzing thousands of posts and controlled uploads. They found that while AI labels are common, the platforms miss many deepfakes and often remove AI source information. The platforms also differ a lot in how they apply labels and communicate about AI content. The authors suggest ways to make AI labeling clearer and more reliable.
What this means in practice
- •For social media platform teams: Improve AI content labeling systems to better detect and transparently mark deepfakes and AI-generated posts following EU guidelines.
- •For digital content compliance teams: Develop clearer reporting and labeling workflows to comply with evolving EU laws on AI-generated and manipulated content.
Authors
Bram Rijsbosch, Luka Bekavac, Henry Tari, Gijs van Dijck, Konrad Kollnig
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.