'Video Unavailable': Analysis and Prediction of Deleted and Moderated YouTube Videos

Maram Kurdi; Nuha Albadi; Shivakant Mishra

doi:10.1109/ASONAM49781.2020.9381310

'Video Unavailable': Analysis and Prediction of Deleted and Moderated YouTube Videos

Maram Kurdi
, Nuha Albadi
, Shivakant Mishra

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

YouTube strives to moderate its content by censoring, demonetizing or removing videos that allegedly violate their community guidelines. Such strategies, especially if seen as unjust by the affected users, could be met with resentment, anger, and in some cases, violence. In addition to YouTube removing videos, uploaders sometimes delete their videos for a variety of reasons such as paraphrasing or preserving online self-image. In this paper, we provide a detailed analysis of deleted/removed videos on YouTube. To do this, we tracked over 73,000 recent YouTube videos for one week and identified those that got deleted or removed. We have then conducted a large-scale analysis of this data and reported on the most informative features that distinguish deleted/removed videos from the ones that remain available. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted/removed at different stages of a video's lifetime, viz., at the time of posting, and after up to seven days have elapsed. Our findings indicate that we can predict video deletion/removal with high accuracy even at the time of posting-a strategy that could help users perceive the removal of their videos as fair as well as reduce public and moderators exposure to problematic videos.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Editors	Martin Atzmuller, Michele Coscia, Rokia Missaoui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	166-173
Number of pages	8
ISBN (Electronic)	9781728110561
DOIs	https://doi.org/10.1109/ASONAM49781.2020.9381310
State	Published - 7 Dec 2020
Externally published	Yes
Event	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands Duration: 7 Dec 2020 → 10 Dec 2020

Publication series

Name	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Country/Territory	Netherlands
City	Virtual, Online
Period	7/12/20 → 10/12/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

YouTube
deleted videos
deletion
prediction

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Information Systems and Management
Social Psychology
Communication

Access to Document

10.1109/ASONAM49781.2020.9381310

Cite this

Kurdi, M., Albadi, N., & Mishra, S. (2020). 'Video Unavailable': Analysis and Prediction of Deleted and Moderated YouTube Videos. In M. Atzmuller, M. Coscia, & R. Missaoui (Eds.), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 (pp. 166-173). Article 9381310 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM49781.2020.9381310

Kurdi, Maram ; Albadi, Nuha ; Mishra, Shivakant. / 'Video Unavailable' : Analysis and Prediction of Deleted and Moderated YouTube Videos. Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. editor / Martin Atzmuller ; Michele Coscia ; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 166-173 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

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title = "'Video Unavailable': Analysis and Prediction of Deleted and Moderated YouTube Videos",

abstract = "YouTube strives to moderate its content by censoring, demonetizing or removing videos that allegedly violate their community guidelines. Such strategies, especially if seen as unjust by the affected users, could be met with resentment, anger, and in some cases, violence. In addition to YouTube removing videos, uploaders sometimes delete their videos for a variety of reasons such as paraphrasing or preserving online self-image. In this paper, we provide a detailed analysis of deleted/removed videos on YouTube. To do this, we tracked over 73,000 recent YouTube videos for one week and identified those that got deleted or removed. We have then conducted a large-scale analysis of this data and reported on the most informative features that distinguish deleted/removed videos from the ones that remain available. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted/removed at different stages of a video's lifetime, viz., at the time of posting, and after up to seven days have elapsed. Our findings indicate that we can predict video deletion/removal with high accuracy even at the time of posting-a strategy that could help users perceive the removal of their videos as fair as well as reduce public and moderators exposure to problematic videos.",

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Kurdi, M , Albadi, N & Mishra, S 2020, 'Video Unavailable': Analysis and Prediction of Deleted and Moderated YouTube Videos. in M Atzmuller, M Coscia & R Missaoui (eds), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020., 9381310, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 166-173, 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Virtual, Online, Netherlands, 7/12/20. https://doi.org/10.1109/ASONAM49781.2020.9381310

'Video Unavailable': Analysis and Prediction of Deleted and Moderated YouTube Videos. / Kurdi, Maram ; Albadi, Nuha; Mishra, Shivakant.
Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. ed. / Martin Atzmuller; Michele Coscia; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. p. 166-173 9381310 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - 'Video Unavailable'

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AU - Kurdi, Maram

AU - Albadi, Nuha

AU - Mishra, Shivakant

PY - 2020/12/7

Y1 - 2020/12/7

N2 - YouTube strives to moderate its content by censoring, demonetizing or removing videos that allegedly violate their community guidelines. Such strategies, especially if seen as unjust by the affected users, could be met with resentment, anger, and in some cases, violence. In addition to YouTube removing videos, uploaders sometimes delete their videos for a variety of reasons such as paraphrasing or preserving online self-image. In this paper, we provide a detailed analysis of deleted/removed videos on YouTube. To do this, we tracked over 73,000 recent YouTube videos for one week and identified those that got deleted or removed. We have then conducted a large-scale analysis of this data and reported on the most informative features that distinguish deleted/removed videos from the ones that remain available. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted/removed at different stages of a video's lifetime, viz., at the time of posting, and after up to seven days have elapsed. Our findings indicate that we can predict video deletion/removal with high accuracy even at the time of posting-a strategy that could help users perceive the removal of their videos as fair as well as reduce public and moderators exposure to problematic videos.

AB - YouTube strives to moderate its content by censoring, demonetizing or removing videos that allegedly violate their community guidelines. Such strategies, especially if seen as unjust by the affected users, could be met with resentment, anger, and in some cases, violence. In addition to YouTube removing videos, uploaders sometimes delete their videos for a variety of reasons such as paraphrasing or preserving online self-image. In this paper, we provide a detailed analysis of deleted/removed videos on YouTube. To do this, we tracked over 73,000 recent YouTube videos for one week and identified those that got deleted or removed. We have then conducted a large-scale analysis of this data and reported on the most informative features that distinguish deleted/removed videos from the ones that remain available. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted/removed at different stages of a video's lifetime, viz., at the time of posting, and after up to seven days have elapsed. Our findings indicate that we can predict video deletion/removal with high accuracy even at the time of posting-a strategy that could help users perceive the removal of their videos as fair as well as reduce public and moderators exposure to problematic videos.

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Kurdi M , Albadi N, Mishra S. 'Video Unavailable': Analysis and Prediction of Deleted and Moderated YouTube Videos. In Atzmuller M, Coscia M, Missaoui R, editors, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 166-173. 9381310. (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). doi: 10.1109/ASONAM49781.2020.9381310

'Video Unavailable': Analysis and Prediction of Deleted and Moderated YouTube Videos

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this