Actions
  • shareshare
  • link
  • cite
  • add
add
auto_awesome_motion View all 14 versions
Publication . Article . Report . Conference object . Preprint . 2019

Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children

Papadamou, Kostantinos; Papasavva, Antonis; Zannettou, Savvas; Blackburn, Jeremy; Kourtellis, Nicolas; Leontiadis, Ilias; Stringhini, Gianluca; +1 Authors
Open Access
English
Abstract

A large number of the most-subscribed YouTube channels target children of a 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.

Published at the 14th International Conference on Web and Social Media (ICWSM 2020). Please cite the ICWSM version

Subjects by Vocabulary

ACM Computing Classification System: GeneralLiterature_MISCELLANEOUS

Subjects

Computer Science - Social and Information Networks, Computer Science - Computers and Society, Social and Information Networks (cs.SI), Computers and Society (cs.CY), FOS: Computer and information sciences, Computer Science, Computers and Society, Social and Information Networks, Electrical Engineering - Electronic Engineering - Information Engineering, Engineering and Technology

31 references, page 1 of 4

[1] M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. Tensorflow: a system for large-scale machine learning. In OSDI, 2016.

[2] S. Agarwal and A. Sureka. A focused crawler for mining hate and extremism promoting videos on youtube. In ACM Hypertext, 2014. [OpenAIRE]

[3] N. Aggarwal, S. Agrawal, and A. Sureka. Mining youtube metadata for detecting privacy invading harassment and misdemeanor videos. In IEEE PST, 2014. [OpenAIRE]

[4] T. C. Alberto, J. V. Lochter, and T. A. Almeida. Tubespam: Comment spam filtering on youtube. In IEEE ICMLA, 2015. [OpenAIRE]

[5] C. S. Arau´ jo, G. Magno, W. Meira, V. Almeida, P. Hartung, and D. Doneda. Characterizing videos, audience and advertising in youtube channels for kids. In SocInfo, 2017.

[6] F. Benevenuto, T. Rodrigues, A. Veloso, J. Almeida, M. Gonc¸alves, and V. Almeida. Practical detection of spammers and content promoters in online video sharing systems. IEEE Cybernetics, 2012.

[7] R. Brandom. Inside elsagate, the conspiracyfueled war on creepy youtube kids videos. https://www.theverge.com/2017/12/8/16751206/elsagateyoutube-kids-creepy-conspiracy-theory, 2017.

[8] V. Bulakh, C. W. Dunn, and M. Gupta. Identifying fraudulently promoted online videos. In WWW, 2014. [OpenAIRE]

[9] M. Buzzi. Children and youtube: access to safe content. In ACM SIGCHI, 2011.

[10] V. Chaudhary and A. Sureka. Contextual feature based oneclass classifier approach for detecting video response spam on youtube. In IEEE PST, 2013.

Funded by
EC| CONCORDIA
Project
CONCORDIA
Cyber security cOmpeteNCe fOr Research anD InnovAtion
  • Funder: European Commission (EC)
  • Project Code: 830927
  • Funding stream: H2020 | RIA
,
EC| ENCASE
Project
ENCASE
EnhaNcing seCurity And privacy in the Social wEb: a user centered approach for the protection of minors
  • Funder: European Commission (EC)
  • Project Code: 691025
  • Funding stream: H2020 | MSCA-RISE
Validated by funder
,
EC| CONCORDIA
Project
CONCORDIA
Cyber security cOmpeteNCe fOr Research anD InnovAtion
  • Funder: European Commission (EC)
  • Project Code: 830927
  • Funding stream: H2020 | RIA
,
EC| ENCASE
Project
ENCASE
EnhaNcing seCurity And privacy in the Social wEb: a user centered approach for the protection of minors
  • Funder: European Commission (EC)
  • Project Code: 691025
  • Funding stream: H2020 | MSCA-RISE
Validated by funder
Related to Research communities
COVID-19
Download fromView all 5 sources
lock_open
Ktisis
Report . 2019
Providers: Ktisis
moresidebar