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.
A large body of previous work focused on the detection of malicious activity on YouTube. @cite_13 use social network analysis techniques to discover hate and extremist YouTube videos, as well as hidden communities in the ecosystem. @cite_22 develop a binary classifier trained with user and video features for detecting YouTube videos that promote hate and extremism. @cite_25 use video, audio, and textual features for training a k-nearest neighbors classifier for detecting YouTube videos containing violence. @cite_18 perform an in-depth analysis on video comments posted by alt-right channels on YouTube. They conclude that the comments of a video are a better indicator for detecting alt-right videos when compared to the video's title. @cite_23 use video features for detecting videos violating privacy or promoting harassment.
@cite_18 @cite_22 @cite_23 @cite_13 @cite_25
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As of 2018, YouTube, the major online video sharing website, hosts multiple channels promoting right-wing content. In this paper, we observe issues related to hate, violence and discriminatory bias in a dataset containing more than 7,000 videos and 17 million comments. We investigate similarities and differences between users' comments and video content in a selection of right-wing channels and compare it to a baseline set using a three-layered approach, in which we analyze (a) lexicon, (b) topics and (c) implicit biases present in the texts. Among other results, our analyses show that right-wing channels tend to (a) contain a higher degree of words from "negative'' semantic fields, (b) raise more topics related to war and terrorism, and (c) demonstrate more discriminatory bias against Muslims (in videos) and towards LGBT people (in comments). Our findings shed light not only into the collective conduct of the YouTube community promoting and consuming right-wing content, but also into the general behavior of YouTube users. Online video sharing platforms such as YouTube contains several videos and users promoting hate and extremism. Due to low barrier to publication and anonymity, YouTube is misused as a platform by some users and communities to post negative videos disseminating hatred against a particular religion, country or person. We formulate the problem of identification of such malicious videos as a search problem and present a focused-crawler based approach consisting of various components performing several tasks: search strategy or algorithm, node similarity computation metric, learning from exemplary profiles serving as training data, stopping criterion, node classifier and queue manager. We implement a best-first search algorithm and conduct experiments to measure the accuracy of the proposed approach. Experimental results demonstrate that the proposed approach is effective. YouTube is one of the most popular and largest video sharing websites (with social networking features) on the Internet. A significant percentage of videos uploaded on YouTube contains objectionable content and violates YouTube community guidelines. YouTube contains several copyright violated videos, commercial spam, hate and extremism promoting videos, vulgar and pornographic material and privacy invading content. This is primarily due to the low publication barrier and anonymity. We present an approach to identify privacy invading harassment and misdemeanor videos by mining the video metadata. We divide the problem into sub-problems: vulgar video detection, abuse and violence in public places and ragging video detection in school and colleges. We conduct a characterization study on a training dataset by downloading several videos using YouTube API and manually annotating the dataset. We define several discriminatory features for recognizing the target class objects. We employ a one class classifier approach to detect the objectionable video and frame the problem as a recognition problem. Our empirical analysis on test dataset reveals that linguistic features (presence of certain terms and people in the title and description of the main and related videos), popularity based, duration and category of videos can be used to predict the video type. We validate our hypothesis by conducting a series of experiments on evaluation dataset acquired from YouTube. Empirical results reveal that accuracy of proposed approach is more than 80 demonstrating the effectiveness of the approach. We describe a semi-automated system to assist law enforcement and intelligence agencies dealing with cyber-crime related to promotion of hate and radicalization on the Internet. The focus of this work is on mining YouTube to discover hate videos, users and virtual hidden communities. Finding precise information on YouTube is a challenging task because of the huge size of the YouTube repository and a large subscriber base. We present a solution based on data mining and social network analysis (using a variety of relationships such as friends, subscriptions, favorites and related videos) to aid an analyst in discovering insightful and actionable information. Furthermore, we performed a systematic study of the features and properties of the data and hidden social networks which has implications in understanding extremism on Internet. We take a case study based approach and perform empirical validation of the proposed hypothesis. Our approach succeeded in finding hate videos which were validated manually. This paper presents a method for detecting violent content in video sharing sites. The proposed approach operates on a fusion of three modalities: audio, moving image and text data, the latter being collected from the accompanying user comments. The problem is treated as a binary classification task (violent vs non-violent content) on a 9-dimensional feature space, where 7 out of 9 features are extracted from the audio stream. The proposed method has been evaluated on 210 YouTube videos and the overall accuracy has reached 82 .