Protecting Young Users on Social Media: Evaluating the Effectiveness of Content Moderation and Legal Safeguards on Video Sharing Platforms

2025-05-16

AI Summary

This study evaluates the effectiveness of algorithmic safeguards against harmful content for minors on TikTok, YouTube, and Instagram under passive consumption—i.e., without active search. Using controlled experimental accounts simulating 13- and 18-year-old users, we collected and manually annotated 3,000 videos across platforms according to a unified harmful-content taxonomy. Results reveal severe algorithmic failures: on YouTube, the 13-year-old account encountered harmful videos (15% prevalence) after just 3 minutes and 6 seconds—significantly faster than the 18-year-old account (8.17%). Critically, none of the platforms applied meaningful age-differentiated filtering despite explicit age labeling, exposing systemic gaps in age-aware moderation and regulatory vulnerabilities arising from age misrepresentation. Our core contribution is a reproducible, multi-platform behavioral experimentation framework that empirically demonstrates the formal inadequacy of current age-sensitive content moderation mechanisms.

Technology Category

Search and Optimization: Algorithm ConfigurationApplication Domains: Misinformation & Fake NewsNatural Language Processing: Safety and Robustness

Application Category

Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated contentResponsible Web: Human-perceived consequences of algorithmic deployment on the webSecurity and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Evaluating effectiveness of video content moderation for minors
• Assessing exposure to harmful videos on TikTok, YouTube, Instagram
• Identifying gaps in age-based algorithmic filtering systems

Innovation

• Experimental accounts simulate minor users
• Manual assessment of harmful video content
• Evaluate algorithmic filtering effectiveness