Identifying fraudulently promoted online videos

Bulakh, Vlad; Dunn, Christopher; Gupta, Manika

doi:10.1145/2567948.2578996

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…(Sureka 2011) focuses on both user features and comment activity logs to propose formulas/rules that can accurately detect spamming YouTube users. Using similar features, (Bulakh, Dunn, and Gupta 2014) characterize and identify fraudulently promoted YouTube videos. (Chaudhary and Sureka 2013) use only video features, and propose a one-class classifier approach for detecting spam videos.…”

Section: Related Workmentioning

confidence: 99%

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

Papadamou

Papasavva

Zannettou

et al. 2020

ICWSM

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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 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.

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Section: Related Workmentioning

confidence: 99%

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

Papadamou

Papasavva

Zannettou

et al. 2020

ICWSM

View full text Add to dashboard Cite

show abstract

“…Then, for each channel we collect all their videos, hence acquiring a set of 181 additional seed videos; 2) we create a list of 83 keywords 8 by extracting n-grams from the title of videos posted on /r/fullcartoonsonyoutube. Similar to the previous approach, for each keyword, we obtain the first 30 videos as returned by the YouTube's Data API search functionality, hence acquiring another 2,342 seed videos; 3) to obtain a random sample of videos, we use the Random YouTube API 9 , which provides random YouTube video identifiers which we then download using the YouTube Data API. This approach resulted in the acquisition of 8,391 seed random videos; and 4) we also collect the most popular videos in the USA, the UK, Russia, India, and Canada, between November 18 and November 21, 2018, hence acquiring another 500 seed videos.…”

Section: Data Collectionmentioning

confidence: 99%

“…A similar study [32] focuses on both user features and comment activity logs to propose formulas/rules that can accurately detect spamming YouTube users. Using similar features, [9] characterize and identify fraudulently promoted YouTube videos. [11] use only video features, and propose a one-class classifier approach for detecting spam videos.…”

Section: Recommendation Graph Analysismentioning

confidence: 99%

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

Papadamou¹,

Papasavva²,

Zannettou³

et al. 2019

Preprint

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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.

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“…al. [12] in which measurement studies were performed to distinguish fraudulent promoters from legitimate users which was followed by development of supervised machine learning models to differentiate legitimate users from those indulging in fraudulent promotion. Spam campaigns in YouTube is another interesting problem studied by Callaghan et.…”

Section: B Detection Of Malicious User Behaviourmentioning

confidence: 99%

KidsTube: Detection, characterization and analysis of child unsafe content & promoters on YouTube

Kaushal

Saha

Bajaj³

et al. 2016

2016 14th Annual Conference on Privacy, Security and Trust (PST)

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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 videolevel, 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.

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Identifying fraudulently promoted online videos

Cited by 11 publications

References 5 publications

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

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

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

KidsTube: Detection, characterization and analysis of child unsafe content & promoters on YouTube

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