Disturbed YouTube for kids: characterizing and detecting inappropriate videos targeting young children
Files
Accepted manuscript
Date
2020-06-08
DOI
Authors
Papadamou, Konstantinos
Papasavva, Antonis
Zannettou, Savvas
Blackburn, Jeremy
De Cristofaro, Emiliano
Kourtellis, Nicolas
Stringhini, Gianluca
Sirivianos, Michael
Version
Accepted manuscript
OA Version
Citation
Konstantinos Papadamou, Antonis Papasavva, Savvas Zannettou, Jeremy Blackburn, Emiliano De Cristofaro, Nicolas Kourtellis, Gianluca Stringhini, Michael Sirivianos. 2020. "Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children." AAAI Conference on Web and Social Media
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.