Paper Info
Title
Disturbed YouTube for Kids: Characterizing and Detecting Disturbing Content on YouTube.
Abstract
A considerable number of the most-subscribed YouTube channels feature content popular among children of very young age. Hundreds of toddler-oriented channels on YouTube offer inoffensive, well produced, and educational videos. Unfortunately, inappropriate (disturbing) content that targets this demographic is also common. YouTubeu0027s algorithmic recommendation system regrettably suggests inappropriate content because some of it mimics or is derived from otherwise appropriate content. Considering the risk for early childhood development, and an increasing trend in toddleru0027s consumption of YouTube media, this is a worrying problem. While there are many anecdotal reports of the scale of the problem, there is no systematic quantitative measurement. Hence, in this work, we develop a classifier able to detect toddler-oriented inappropriate content on YouTube with 82.8% accuracy, and we leverage it to perform a first-of-its-kind, large-scale, quantitative characterization that reveals some of the risks of YouTube media consumption by young children. Our analysis indicates that YouTubeu0027s currently deployed counter-measures are ineffective in terms of detecting disturbing videos in a timely manner. Finally, using our classifier, we assess how prominent the problem is on YouTube, finding that young children are likely to encounter disturbing videos when they randomly browse the platform starting from benign videos.
Year
Venue
DocType
2019
arXiv: Social and Information Networks
Journal
Volume
Citations 
PageRank 
abs/1901.07046
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Kostantinos Papadamou122.72
Antonis Papasavva201.69
Savvas Zannettou35913.57
Jeremy Blackburn441541.72
Nicolas Kourtellis547538.62
Ilias Leontiadis676144.38
Gianluca Stringhini770161.87
Michael Sirivianos869641.25