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.
Several studies focused on understanding videos that target young children, and how they interact with such videos and the platform. Buzzi @cite_12 suggests the addition of extra parental controls on YouTube in an attempt to prevent children from accessing inappropriate content. Ara 'u @cite_26 study the audience profiles and comments posted on YouTube videos in popular children-oriented channels, and conclude that children under the age of 13 use YouTube and are exposed to advertising, inappropriate content, and privacy issues. @cite_2 propose a binary classifier, based on video metadata, for identifying suitable YouTube videos for children. @cite_24 focus on the characterization and detection of unsafe content for children and its promoters on YouTube. They propose a machine learning classifier that considers a set of video-, user-, and comment-level features for the detection of users that promote unsafe content.
@cite_24 @cite_26 @cite_12 @cite_2
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A considerable number of the most-subscribed YouTube channels feature content popular among children of very young age. Hundreds of toddler-oriented channels on YouTube offer inoffensive, well produced, and educational videos. Unfortunately, inappropriate (disturbing) 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 worrying problem. While there are many anecdotal reports of the scale of the problem, there is no systematic quantitative measurement. Hence, in this work, we develop a classifier able to detect toddler-oriented inappropriate content on YouTube with 82.8 accuracy, and we 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 indicates that YouTube's currently deployed counter-measures are ineffective in terms of detecting disturbing videos in a timely manner. Finally, using our classifier, we assess how prominent the problem is on YouTube, finding that young children are likely to encounter disturbing videos when they randomly browse the platform starting from benign videos. 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 for kids. Through detailed characterization studies, we are able to successfully conclude that unsafe content promoters are less popular and engage less as compared with other users. Finally, using a network of unsafe content promoters and other users based on their engagements (likes, subscription and playlist addition) and other factors, we find that unsafe content is present very close to safe content and unsafe content promoters form very close knit communities with other users, thereby further increasing the likelihood of a child getting getting exposed to unsafe content. Acknowledging the advantages as well as the dangers of the internet content on kids education and entertainment, YouTube Kids was created. Based on regulations for child-friendly programs, several violations are identified and restricted from viewable content. When a child surfs the Internet, the same regulations could be automatically detected and filtered. However, current YouTube Kids content filtering relies on meta-data attributes, where inappropriate content could pass the filtering mechanism. This research, propose an advanced real-time content filtering approach using automated video and audio analysis as an extra layer for kids safety. The proposed method utilizes the thin-slicing theory, where several one second slices are selected randomly from the clip and extracted. The use of a one-second slice will assure a temporal analysis of the clip content, and ensures a real-time content analysis. For each slice, audio is automatically transcribed using automatic speech recognition techniques to be further analysed for its linguistic content. Furthermore, the audio signal is analysed to detect event and scenes (e.g. explosion). The image frames extracted from the slices are also inspected for its content to avoid inappropriate scenes, such as violence. Upon the success of this approach on YouTube Kids application, investigation of its generalizability to other video applications, and other languages could be performed. Online video services, messaging systems, games and social media services are tremendously popular among young people and children in many countries. Most of the digital services offered on the internet are advertising funded, which makes advertising ubiquitous in children’s everyday life. To understand the impact of advertising-based digital services on children, we study the collective behavior of users of YouTube for kids channels and present the demographics of a large number of users. We collected data from 12,848 videos from 17 channels in US and UK and 24 channels in Brazil. The channels in English have been viewed more than 37 billion times. We also collected more than 14 million comments made by users. Based on a combination of text-analysis and face recognition tools, we show the presence of racial and gender biases in our large sample of users. We also identify children actively using YouTube, although the minimum age for using the service is 13 years in most countries. We provide comparisons of user behavior among the three countries, which represent large user populations in the global North and the global South.