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.
With regard to spam detection, @cite_10 explore video attributes that may enable the detection of spam videos on YouTube. A similar study by Sureka @cite_20 focuses on both user features and comment activity logs to propose formulas rules that can accurately detect spamming YouTube users. Using similar features, @cite_29 characterize and identify fraudulently promoted YouTube videos. @cite_15 use only video features, and propose a one-class classifier approach for detecting spam videos.
@cite_29 @cite_10 @cite_20 @cite_15
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Fraudulent product promotion online, including online videos, is on the rise. In order to understand and defend against this ill, we engage in the fraudulent video economy for a popular video sharing website, YouTube, and collect a sample of over 3,300 fraudulently promoted videos and 500 bot profiles that promote them. We then characterize fraudulent videos and profiles and train supervised machine learning classifiers that can successfully differentiate fraudulent videos and profiles from legitimate ones. People now feel more comfortable socializing over the internet through popular social networking and media websites than face to face. Thus, the social media websites are thriving more and more nowadays. Like others YouTube is a vastly popular social media site which is expanding at very fast pace. YouTube depends mostly on user created contents and sharing and spreading. Business entities and public figures are taking advantage of this popularity by creating their own page and shared information among the large number of visitors. However, due to this popularity, YouTube has become more susceptible to different types of unwanted and malicious spammer. Currently, YouTube does not have any way to handle its video spammers. It only considers mass comments or messages to be part of spamming. To increase the popularity of a video, malicious users post video response spam, where the video content is not related to the topic being discussed in the particular video or does not contain the media it is supposed to. In this research, we explore different attributes that could lead to video spammers. We first collect data of YouTube videos and manually classify them as either legitimate videos or spams. We then devise a number of attributes of videos which could potentially be used to detect spams. We apply Microsoft SQL Server Data Mining Tools (SSDT) to provide a heuristic for classifying an arbitrary video as either spam or legitimate. Our result demonstrates that in the long run we could successfully classify videos as spam or legitimate videos for most of the cases. Research shows that comment spamming (comments which are unsolicited, unrelated, abusive, hateful, commercial advertisements etc) in online discussion forums has become a common phenomenon in Web 2.0 applications and there is a strong need to counter or combat comment spamming. We present a method to automatically detect comment spammer in YouTube (largest and a popular video sharing website) forums. The proposed technique is based on mining comment activity log of a user and extracting patterns (such as time interval between subsequent comments, presence of exactly same comment across multiple unrelated videos) indicating spam behavior. We perform empirical analysis on data crawled from YouTube and demonstrate that the proposed method is effective for the task of comment spammer detection. YouTube is one of the largest video sharing websites (with social networking features) on the Internet. The immense popularity of YouTube, anonymity and low publication barrier has resulted in several forms of misuse and video pollution such as uploading of malicious, copyright violated and spam video or content. YouTube has a popular and commonly used feature called as video response which allows users to post a video response to an uploaded or existing video. Some of the popular videos on YouTube receive thousands of video responses. We observe presence of opportunistic users posting unrelated, promotional, pornographic videos (spam videos posted manually or using automated scripts) as video responses to existing videos. We present a method of mining YouTube to automatically detect video response spam. We formulate the problem of video response spam detection as a one-class classification problem (a recognition task) and divide the problem into three sub-problems: promotional video recognition, pornographic or dirty video recognition and automated script or botnet uploader recognition. We create a sample dataset of target class videos for each of the three sub-problems and identify contextual features (meta-data based or non-content based features) characterizing the target class. Our empirical analysis reveals that certain linguistic features (presence of certain terms in the title or description of the YouTube video), temporal features, popularity based features, time based features can be used to predict the video type. We identify features with discriminatory powers and use it within a one-class classification framework to recognize video response spam. We conduct a series of experiments to validate the proposed approach and present evidences to demonstrate the effectiveness of the proposed solution with more than 80 accuracy.