Keep It PG or Let It Go?

Exploring the Presence of (In)appropriateness in YouTube Videos for Young Children and Opportunities for Safeguarding

Master Thesis (2025)
Author(s)

J.J.P. de Water (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Maria S. Pera – Mentor (TU Delft - Web Information Systems)

Robin Ungruh – Mentor (TU Delft - Web Information Systems)

Cynthia C. S. Liem – Graduation committee member (TU Delft - Multimedia Computing)

Faculty
Electrical Engineering, Mathematics and Computer Science
More Info
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Publication Year
2025
Language
English
Graduation Date
30-04-2025
Awarding Institution
Delft University of Technology
Programme
['Computer Science']
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

As online video platforms like YouTube and YouTube Kids continue to shape young children's daily media use, concerns about their exposure to inappropriate content persist. While these platforms implement various safeguards to protect young audiences, inappropriate videos continue to surface in search results and next-video recommendations, sometimes even at the top of the list. This study explores how metadata-derived features can be used to identify and address such content. Drawing on a ground-truth dataset of YouTube videos labeled for toddler appropriateness, we conduct a detailed feature analysis to uncover patterns linked to (in)appropriateness and train a classifier capable of predicting video appropriateness for young children. Building on these insights, we develop and evaluate score-based reranking strategies designed to reduce exposure to inappropriate videos while promoting age-appropriate content. Our findings show that metadata-informed reranking significantly improves the prioritization of suitable content, raising HitRate@1 of suitable videos from 14% to as high as 62%, but also reveal critical trade-offs: misclassified inappropriate videos, particularly when predicted with high confidence, may still appear in top-ranked positions. As such, detection and reranking methods like ours represent a first step that warrants further steps for safer recommendation environments for young children. This research provides a practical framework for improving recommendation outcomes and contributes to the broader conversation on designing safer, more transparent, and child-centered media systems.

Full Codebase available on the following repository - https://github.com/JoeydeW/KeepItPGorLetItGo

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