KidsTube: Detection, Characterization and Analysis of Child Unsafe Content & Promoters on YouTube
Rishabh Kaushal, Srishty Saha, Payal Bajaj, Ponnurangam Kumaraguru

TL;DR
This paper presents methods to detect and analyze unsafe content and promoters targeting children on YouTube, achieving high accuracy and revealing community structures that facilitate unsafe content exposure.
Contribution
It introduces a combined supervised and CNN-based approach for detecting unsafe child content and characterizes the behavior and community structure of unsafe content promoters.
Findings
Detection accuracy of 85.7% achieved
Unsafe promoters are less popular and less engaged
Unsafe content is closely clustered with safe content
Abstract
YouTube draws large number of users who contribute actively by uploading videos or commenting on existing videos. However, being a crowd sourced and large content pushed onto it, there is limited control over the content. This makes malicious users push content (videos and comments) which is inappropriate (unsafe), particularly when such content is placed around cartoon videos which are typically watched by kids. In this paper, we focus on presence of unsafe content for children and users who promote it. For detection of child unsafe content and its promoters, we perform two approaches, one based on supervised classification which uses an extensive set of video-level, user-level and comment-level features and another based Convolutional Neural Network using video frames. Detection accuracy of 85.7% is achieved which can be leveraged to build a system to provide a safe YouTube experience…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Hate Speech and Cyberbullying Detection · Spam and Phishing Detection
