Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children
Date Issued
January 21, 2019
Abstract
A large number of the most-subscribed YouTube channels target children of
very young age. Hundreds of toddler-oriented channels on YouTube feature
inoffensive, well produced, and educational videos. Unfortunately,
inappropriate content that targets this demographic is also common. YouTube's
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 toddler's consumption of YouTube media, this is a worrisome problem.
In this work, we build a classifier able to discern inappropriate content
that targets toddlers on YouTube with 84.3% accuracy, and 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 reveals that YouTube is still plagued by such disturbing videos and
its currently deployed counter-measures are ineffective in terms of detecting
them in a timely manner. Alarmingly, using our classifier we show that young
children are not only able, but likely to encounter disturbing videos when they
randomly browse the platform starting from benign videos.
very young age. Hundreds of toddler-oriented channels on YouTube feature
inoffensive, well produced, and educational videos. Unfortunately,
inappropriate content that targets this demographic is also common. YouTube's
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 toddler's consumption of YouTube media, this is a worrisome problem.
In this work, we build a classifier able to discern inappropriate content
that targets toddlers on YouTube with 84.3% accuracy, and 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 reveals that YouTube is still plagued by such disturbing videos and
its currently deployed counter-measures are ineffective in terms of detecting
them in a timely manner. Alarmingly, using our classifier we show that young
children are not only able, but likely to encounter disturbing videos when they
randomly browse the platform starting from benign videos.
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