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.

本文研究了 YouTube 上针对幼儿的视频内容存在的问题及其对早期儿童发展的风险，构建了一个分类器以准确识别 YouTube 上针对幼儿的不适宜内容，并揭示了该平台所存在的一些风险。

干扰儿童 YouTube：对针对幼儿的不适当视频进行特征化和检测

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

How can web services that depend on user generated content discern fake
social engagement activities by spammers from legitimate ones? In this paper,
we focus on the social site of YouTube and the problem of identifying bad
actors posting inorganic contents and inflating the count of social engagement
metrics. We propose an effective method, Leas (Local Expansion at Scale), and
show how the fake engagement activities on YouTube can be tracked over time by
analyzing the temporal graph based on the engagement behavior pattern between
users and YouTube videos. With the domain knowledge of spammer seeds, we
formulate and tackle the problem in a semi-supervised manner --- with the
objective of searching for individuals that have similar pattern of behavior as
the known seeds --- based on a graph diffusion process via local spectral
subspace. We offer a fast, scalable MapReduce deployment adapted from the
localized spectral clustering algorithm. We demonstrate the effectiveness of
our deployment at Google by achieving an manual review accuracy of 98% on
YouTube Comments graph in practice. Comparing with the state-of-the-art
algorithm CopyCatch, Leas achieves 10 times faster running time. Leas is
actively in use at Google, searching for daily deceptive practices on YouTube's
engagement graph spanning over a billion users.

本文介绍了一种名为 Leas 的有效方法，用于追踪在 YouTube 上的虚假社交交互活动，利用图谱扩展的过程和基于用户和 YouTube 视频之间的行为模式的时间图谱分析来解决问题。我们的方法在 Google 部署时表现良好，其手动审核的准确性可达 98％，比现有算法快 10 倍。