Proceedings of the Fourteenth International AAAI Conference on Web and Social Media (ICWSM 2020) Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children Kostantinos Papadamou, Antonis Papasavva, Savvas Zannettou,∗ Jeremy Blackburn,† Nicolas Kourtellis,‡ Ilias Leontiadis,‡ Gianluca Stringhini, Michael Sirivianos Cyprus University of Technology, ∗Max-Planck-Institut fur¨ Informatik, †Binghamton University, ‡Telefonica Research, Boston University {ck.papadamou, as.papasavva}@edu.cut.ac.cy,
[email protected],
[email protected] {nicolas.kourtellis, ilias.leontiadis}@telefonica.com,
[email protected],
[email protected] Abstract A large number of the most-subscribed YouTube channels tar- get 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 algo- rithmic recommendation system regrettably suggests inap- propriate content because some of it mimics or is derived Figure 1: Examples of disturbing videos, i.e. inappropriate from otherwise appropriate content. Considering the risk for early childhood development, and an increasing trend in tod- videos that target toddlers. dler’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 Frozen, Mickey Mouse, etc., combined with disturbing con- 84.3% accuracy, and leverage it to perform a large-scale, tent containing, for example, mild violence and sexual con- quantitative characterization that reveals some of the risks of notations. These disturbing videos usually include an inno- YouTube media consumption by young children. Our analy- cent thumbnail aiming at tricking the toddlers and their cus- sis reveals that YouTube is still plagued by such disturbing todians.