ABSTRACT
Short-form video platforms expose young audiences to algorithmically amplified content, yet systematic, feed-level evidence from TikTok remains scarce. This study analyzes 1,000 TikTok videos collected via a clean, non-interacting 13-year-old profile and classifies each video into one dominant category, recording likes, comments, saves, and shares to map risks and engagement at scale. Entertainment and Arts category predominated overall, with significant between-category differences in all interaction metrics (Kruskal-Wallis), reflecting right-skewed distributions and niche virality patterns. Potentially dangerous content constituted 10.5% of the sample, peaking in Education and Information category (18%), followed by Entertainment and Arts (12.3%) and Lifestyle and Personal Content (10.4%). Domestic SK/CZ videos contained more potentially dangerous content than foreign videos (12.1% vs 7.4%; χ²(1)=5.14, p=.023, V=0.072). Potentially dangerous videos received significantly fewer likes and saves than safe videos, while differences in comments and shares were nonsignificant, with a modestly higher median of shares for this content suggesting networked forwarding rather than broad popularity. The leading dangerous subtypes were sexual/sexualized content (26.7%), hate and discrimination (21.9%), and misinformation/manipulation (18.1%). Findings support targeted platform moderation and age-appropriate design alongside media-literacy interventions prioritized for sexualization, hate speech, and misinformation, with attention to locally trending content.
KEY WORDS
Dangerous Videos. Media Literacy. Taxonomic Classification. TikTok. Youth.
DOI
https://doi.org/10.34135/mlar-25-02-03
What Minors See on Tiktok: Taxonomy, Prevalence, and Interaction Patterns of Potentially Dangerous Videos © 2025 by Lucia Novanská Škripcová, UCM Trnava is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International license.