KidsTube: Detection, Characterization and Analysis of Child Unsafe Content &amp; Promoters on YouTube

Kaushal, Rishabh; Saha, Srishty; Bajaj, Payal; Kumaraguru, Ponnurangam

doi:10.48550/arxiv.1608.05966

Cited by 3 publications

(9 citation statements)

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“…The study conducted by Papadamou et al (2020) found that children are likely to encounter disturbing content via the recommender system from videos classified as appropriate for children aged one to five. However, the studies conducted by Kaiser and Rauchfleisch (2019) and Kaushal et al (2016) found mixed results. For example, Kaiser and Rauchfleisch (2019) created a directed graph of nodes (videos) and edges (video recommendations).…”

Section: Resultsmentioning

confidence: 98%

“…Two studies did not implicate the YouTube recommender system in facilitating problematic content pathways ( Hosseinmardi et al, 2020 ; Ledwich & Zaitsev, 2019 ). Finally, seven studies produced mixed results ( Abul-Fottouh, Song, & Gruzd, 2020 ; Faddoul, Chaslot, & Farid, 2020 ; Kaiser & Rauchfleisch, 2019 ; Kaushal et al, 2016 ; Papadamou et al, 2020 ; Ribeiro et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

“… Kaushal et al (2016) found that safe-to-unsafe content transitions are rare, but un-safe-to-unsafe transitions are common. The study conducted by Stöcker and Preuss (2020) simulated users consuming content via the recommender system.…”

Section: Resultsmentioning

confidence: 99%

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Systematic review: YouTube recommendations and problematic content

Yesilada

Lewandowsky

2022

Internet Policy Review

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Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

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Systematic review: YouTube recommendations and problematic content

Yesilada

Lewandowsky

2022

Internet Policy Review

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“…Countering such panics, research into YouTube indicates that its potential to radicalise is limited by users' prior inclinations and political orientations (Faddoul, et al,, 2020), with searches and channel subscriptions often being more decisive for the political content people watch (e.g., Papadamou, et al, 2022;Chen, Nyhan, et al, 2023;Kaushal, et al, 2016). In this light, what seems to become reproduced in the unfolding dynamic between current research on YouTube and the more popular critiques in news media is a ritual in which academic research takes over the function of reigning in and delimiting the socialising powers that news media frequently attribute to new media.…”

Section: Pacifying the Active Usermentioning

confidence: 99%

Have your cake and feed it forward too: YouTube, oral cravings and the persistent question of media addiction

Krüger

2023

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Focusing on the case of recommendations on the video streaming platform YouTube, this article revisits questions of media addiction and addictive media that continue to trouble research in the field. Based on a close reading of Google/YouTube’s engineering papers, this paper argues that the platform’s recommender system — the machine learning system responsible for the personalisation and customisation of what videos users are offered — has been designed to function as a feeding tube and a precarious holding environment, thus corroborating widespread critiques about this system’s addictive — oral — strategies of user retention. Subsequently, this article discusses how the platform’s more recent promise of “responsible recommendations” has so far been articulated in the engineering papers. Specifically, this promise has taken the form of a fetishist structure that endorses responsibility but not at the expense of the time users spend watching. This structure is best captured in the proverbial Have your cake and eat it too.

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“…Malicious videos intentionally include audiovisual features which captivate the viewer's attention such as rapid repetitive motion, disgusting and scary characters, disturbing noise, and loud sounds. Kaushal et al (2016) note that a child who selects a recommended malicious video is more prone to continue watching a series of similar videos based on the video recommender system. This problem is of grave concern since 1) these children are unable to distinguish the appropriateness of the video content by themselves and 2) their parents are unable to constantly supervise their viewing (Ovide 2021).…”

Section: Introductionmentioning

confidence: 99%

Malicious or Benign? Towards Effective Content Moderation for Children's Videos

Ahmed,

Khan,

Qaisar

et al. 2023

FLAIRS

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Online video platforms receive hundreds of hours of uploads every minute, making manual content moderation impossible. Unfortunately, the most vulnerable consumers of malicious video content are children from ages 1-5 whose attention is easily captured by bursts of color and sound. Scammers attempting to monetize their content may craft malicious children's videos that are superficially similar to educational videos, but include scary and disgusting characters, violent motions, loud music, and disturbing noises. Prominent video hosting platforms like YouTube have taken measures to mitigate malicious content on their platform, but these videos often go undetected by current content moderation tools that are focused on removing pornographic or copyrighted content. This paper introduces our toolkit (Malicious or Benign) for promoting research on automated content moderation of children's videos. We present 1) a customizable annotation tool for videos, 2) a new dataset with difficult to detect test cases of malicious content and 3) a benchmark suite of state-of-the-art video classification models.

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KidsTube: Detection, Characterization and Analysis of Child Unsafe Content & Promoters on YouTube

Cited by 3 publications

References 0 publications

Systematic review: YouTube recommendations and problematic content

Systematic review: YouTube recommendations and problematic content

Have your cake and feed it forward too: YouTube, oral cravings and the persistent question of media addiction

Malicious or Benign? Towards Effective Content Moderation for Children's Videos

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