Protecting Young Users on Social Media: Evaluating the Effectiveness of Content Moderation and Legal Safeguards on Video Sharing Platforms
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Abstract (arXiv)
Video-sharing social media platforms, such as TikTok, YouTube, and Instagram, implement content moderation policies aimed at reducing exposure to harmful videos among minor users. As video has become the dominant and most immersive form of online content, understanding how effectively this medium is moderated for younger audiences is urgent. In this study, we evaluated the effectiveness of video moderation for different age groups on three of the main video-sharing platforms: TikTok, YouTube, and Instagram. We created experimental accounts for the children assigned ages 13 and 18. Using these accounts, we evaluated 3,000 videos served up by the social media platforms, in passive scrolling and search modes, recording the frequency and speed at which harmful videos were encountered. Each video was manually assessed for level and type of harm, using definitions from a unified framework of harmful content. The results show that for passive scrolling or search-based scrolling, accounts assigned to the age 13 group encountered videos that were deemed harmful, more frequently and quickly than those assigned to the age 18 group. On YouTube, 15% of recommended videos to 13-year-old accounts during passive scrolling were assessed as harmful, compared to 8.17% for 18-year-old accounts. On YouTube, videos labelled as harmful appeared within an average of 3:06 minutes of passive scrolling for the younger age group. Exposure occurred without user-initiated searches, indicating weaknesses in the algorithmic filtering systems. These findings point to significant gaps in current video moderation practices by social media platforms. Furthermore, the ease with which underage users can misrepresent their age demonstrates the urgent need for more robust verification methods.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 May 2025.
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Citing papers per year: 2026: 3.
3 papers.
- 2026When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigmcited by 2
- 2026SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screeningcited by 0
- 2026Child-Oriented AIGC Video Risk Reviewing: A Benchmark and Knowledge-Supported Iterative Reasoning Frameworkcited by 0
- 2026Child-Oriented AIGC Video Risk Reviewing: A Benchmark and Knowledge-Supported Iterative Reasoning Framework21 Jul 2026
- 2026SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening17 May 2026
- 2026When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm25 Mar 2026
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- 2025Towards an Automated Framework to Audit Youth Safety on TikTok
- 2025MTikGuard System: A Transformer-Based Multimodal System for Child-Safe Content Moderation on TikTok
- 2024Harmful YouTube Video Detection: A Taxonomy of Online Harm and MLLMs as Alternative Annotators
- 2025Towards Safer Social Media Platforms: Scalable and Performant Few-Shot Harmful Content Moderation Using Large Language Models
- 2022Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild
- 2026When Youth Enter the Algorithmic Wild: Discovering and Understanding Potentially Harmful Teen Videos on Douyin and Kwai