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“Think before you upload”: an in-depth analysis of unavailable videos on YouTube

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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 videos removed by YouTube or deleted by the uploader. To do this, we tracked over 73,000 recent YouTube videos for 1 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, as well as the features that distinguish videos removed by YouTube from those deleted by the uploader. Based on our analysis, we have developed machine learning prediction models that predict videos that will get deleted or removed at three different stages of a video’s lifetime, namely at the time of posting, 3 h after a video has been posted, and after up to 7 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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  1. https://developers.google.com/youtube/v3/

References

  • Agarwal S, Sureka A (2014) A focused crawler for mining hate and extremism promoting videos on youtube. In: Proceedings of the 25th ACM conference on Hypertext and social media. ACM, pp 294–296

  • Albadi N, Kurdi M, Mishra S (2018) Are they our brothers? Analysis and detection of religious hate speech in the Arabic Twittersphere. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, pp 69–76

  • Albadi N, Kurdi M, Mishra S (2019a) Hateful people or hateful bots? Detection and characterization of bots spreading religious hatred in Arabic social media. Proc ACM Hum Comput Interact 3(CSCW):1–25

    Article  Google Scholar 

  • Albadi N, Kurdi M, Mishra S (2019b) Investigating the effect of combining GRU neural networks with handcrafted features for religious hatred detection on Arabic twitter space. Soc Netw Anal Min 9(1):41

    Article  Google Scholar 

  • Almuhimedi H, Wilson S, Liu B, Sadeh N, Acquisti A (2013) Tweets are forever: a large-scale quantitative analysis of deleted tweets. In: Proceedings of the 2013 conference on Computer supported cooperative work. ACM, pp 897–908

  • Asarch S (2018) How youtube censorship bots are crashing creator careers. https://www.newsweek.com/youtube-censorship-bots-mumkey-jones-algorithm-1265776. Accessed 4 Sep 2019

  • Bagdouri M, Oard DW (2015) On predicting deletions of microblog posts. In: Proceedings of the 24th ACM international on conference on information and knowledge management. ACM, pp 1707–1710

  • Beckett J (2018) We need to talk about the mental health of content moderators. http://theconversation.com/we-need-to-talk-about-the-mental-health-of-content-moderators-103830. Accessed 5 Jan 2020

  • Bhattacharya P, Ganguly N (2016) Characterizing deleted tweets and their authors. In: Tenth international AAAI conference on web and social media

  • Bickert M (2018) Publishing our internal enforcement guidelines and expanding our appeals process. https://about.fb.com/news/2018/04/comprehensive-community-standards/. Accessed 15 Dec 2019

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Chancellor S, Lin ZJ, De Choudhury M (2016) This post will just get taken down: characterizing removed pro-eating disorder social media content. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, pp 1157–1162

  • Chandrasekharan E, Gandhi C, Mustelier MW, Gilbert E (2019) Crossmod: a cross-community learning-based system to assist reddit moderators. Proc ACM Hum Comput Interact 3(CSCW):174

    Article  Google Scholar 

  • Chang J, Danescu-Niculescu-Mizil C (2019) Trajectories of blocked community members: Redemption, recidivism and departure. In: The world wide web conference, pp 184–195

  • Chatzopoulou G, Sheng C, Faloutsos M (2010) A first step towards understanding popularity in youtube. In: 2010 INFOCOM IEEE conference on computer communications workshops. IEEE, pp 1–6

  • Church KW, Hanks P (1990) Word association norms, mutual information, and lexicography. Comput Linguist 16(1):22–29

    Google Scholar 

  • Coldewey D, Hatmaker T (2018) Police say shooter’s anger over youtube policies appears to be the motive. https://techcrunch.com/2018/04/04/police-say-shooters-anger-over-youtube-policies-appears-to-be-the-motive. Accessed 4 Sep 2019

  • Crossfield J (2019) The hidden consequences of moderating social media’s dark side. https://contentmarketinginstitute.com/cco-digital/july-2019/social-media-moderators-stress/. Accessed 5 Jan 2020

  • Davidson T, Warmsley D, Macy M, Weber I (2017) Automated hate speech detection and the problem of offensive language. In: Eleventh international AAAI conference on web and social media. IEEE

  • Dry J (2016) Youtube creators cry censorship as ‘inappropriate’ content is no longer monetizable on the platform. https://www.indiewire.com/2016/09/youtube-censorship-inappropriate-content-guidelines-twitter-1201722209/. Accessed 8 Jan 2020

  • Ekman M (2014) The dark side of online activism: Swedish right-wing extremist video activism on youtube. MedieKultur J Media Commun Res 30(56):21

    Article  Google Scholar 

  • Figueiredo F, Benevenuto F, Almeida JM (2011) The tube over time: characterizing popularity growth of youtube videos. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 745–754

  • Google (2019a) Report inappropriate content. https://support.google.com/youtube/answer/2802027. Accessed 4 Sep 2019

  • Google (2019b) Youtube community guidelines enforcement. https://transparencyreport.google.com/youtube-policy/removals. Accessed 3 June 2019

  • Grimmelmann J (2015) The virtues of moderation. Yale JL & Tech 17:42

    Google Scholar 

  • Hale J (2019) More than 500 hours of content are now being uploaded to youtube every minute. https://rb.gy/k6vjam. Accessed 9 Dec 2019

  • Hutto CJ, Gilbert E (2014) Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth international AAAI conference on weblogs and social media

  • Jhaver S, Appling DS, Gilbert E, Bruckman A (2019a) “Did you suspect the post would be removed?” Understanding user reactions to content removals on reddit. Proc ACM Hum Comput Interact 3(CSCW):1–33

    Google Scholar 

  • Jhaver S, Birman I, Gilbert E, Bruckman A (2019b) Human–machine collaboration for content regulation: The case of reddit automoderator. ACM Trans Comput Hum Interact (TOCHI) 26(5):1–35

    Article  Google Scholar 

  • Jhaver S, Bruckman A, Gilbert E (2019c) Does transparency in moderation really matter? User behavior after content removal explanations on reddit. Proc ACM Hum Comput Interact 3(CSCW):150

    Google Scholar 

  • Kaji N, Kitsuregawa M (2007) Building lexicon for sentiment analysis from massive collection of HTML documents. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), pp 1075–1083

  • Kaushal R, Saha S, Bajaj P, Kumaraguru P (2016) Kidstube: detection, characterization and analysis of child unsafe content & promoters on youtube. In: 2016 14th annual conference on privacy. Security and trust (PST). IEEE, pp 157–164

  • Kiene C, Jiang JA, Hill BM (2019) Technological frames and user innovation: exploring technological change in community moderation teams. Proc ACM Hum Comput Interact 3(CSCW):44

    Article  Google Scholar 

  • Kurdi M, Albadi N, Mishra S (2020) “video unavailable”: analysis and prediction of deleted and moderated youtube videos. In: 2020 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 166–173

  • de Laat PB (2012) Coercion or empowerment? Moderation of content in Wikipedia as ‘essentially contested’ bureaucratic rules. Ethics Inf Technol 14(2):123–135

    Article  Google Scholar 

  • Madden M, Lenhart A, Cortesi S, Gasser U, Duggan M, Smith A, Beaton M (2013) Teens, social media, and privacy. Pew Res Center 21:2–86

    Google Scholar 

  • Mariconti E, Suarez-Tangil G, Blackburn J, De Cristofaro E, Kourtellis N, Leontiadis I, Serrano JL, Stringhini G (2019) “you know what to do” proactive detection of youtube videos targeted by coordinated hate attacks. Proc ACM Hum Comput Interact 3(CSCW):1–21

    Article  Google Scholar 

  • Mohsin M (2019) 10 youtube stats every marketer should know in 2020. https://www.oberlo.com/blog/youtube-statistics. Accessed 8 Jan 2020

  • Mosseri A (2019) Our commitment to lead the fight against online bullying. https://instagram-press.com/blog/2019/07/08/our-commitment-to-lead-the-fight-against-online-bullying/. Accessed 5 Jan 2020

  • Myers West S (2018) Censored, suspended, shadowbanned: user interpretations of content moderation on social media platforms. New Media Soc 20(11):4366–4383

    Article  Google Scholar 

  • Ottoni R, Cunha E, Magno G, Bernardina P, Meira Jr W, Almeida V (2018) Analyzing right-wing youtube channels: hate, violence and discrimination. In: Proceedings of the 10th ACM conference on web science. ACM, pp 323–332

  • Papadamou K, Papasavva A, Zannettou S, Blackburn J, Kourtellis N, Leontiadis I, Stringhini G, Sirivianos M (2019) Disturbed youtube for kids: characterizing and detecting disturbing content on youtube. arXiv preprint arXiv:190107046

  • Pearson K (1900) X. on the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Lond Edinb Dublin Philos Mag J Sci 50(302):157–175

    Article  Google Scholar 

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  • Petrovic S, Osborne M, Lavrenko V (2013) I wish i didn’t say that! analyzing and predicting deleted messages in twitter. arXiv preprint arXiv:13053107

  • Pinto H, Almeida JM, Gonçalves MA (2013) Using early view patterns to predict the popularity of youtube videos. In: Proceedings of the sixth ACM international conference on Web search and data mining. ACM, pp 365–374

  • Policy TP (2018) Evolving our twitter transparency report: expanded data and insights. https://blog.twitter.com/official/en_us/topics/company/2018/evolving-our-twitter-transparency-report.html. Accessed 4 Sep 2019

  • Preece J, Maloney-Krichmar D (2003) Online communities: focusing on sociability and usability. In: Handbook of human–computer interaction, pp 596–620

  • Rosenfeld E (2018) Shooter allegedly targeted youtube hq because she ‘hated’ the company for blocking her videos. https://www.cnbc.com/2018/04/03/youtube-shooter-identified-as-nasim-aghdam.html. Accessed 4 Sep 2019

  • Schultes P, Dorner V, Lehner F (2013) Leave a comment! An in-depth analysis of user comments on youtube. Wirtschaftsinformatik 42:659–673

    Google Scholar 

  • Shutterstock, Inc (2015) List of dirty, naughty, obscene, and otherwise bad words. https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words. Accessed 10 Jan 2019

  • Siersdorfer S, Chelaru S, Nejdl W, San Pedro J (2010) How useful are your comments? Analyzing and predicting youtube comments and comment ratings. In: Proceedings of the 19th international conference on world wide web. ACM, pp 891–900

  • Sleeper M, Cranshaw J, Kelley PG, Ur B, Acquisti A, Cranor LF, Sadeh N (2013) I read my twitter the next morning and was astonished: a conversational perspective on twitter regrets. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 3277–3286

  • Soni D, Singh VK (2018) See no evil, hear no evil: audio-visual-textual cyberbullying detection. Proc ACM Hum Comput Interact 2(CSCW):164

    Article  Google Scholar 

  • Srinivasan KB, Danescu-Niculescu-Mizil C, Lee L, Tan C (2019) Content removal as a moderation strategy: Compliance and other outcomes in the changemyview community. Proc ACM Hum Comput Interact 3(CSCW):163

    Article  Google Scholar 

  • Statt N (2019) Man arrested for showing up at youtube and threatening violence over deleted account. https://www.theverge.com/2019/3/12/18262853/youtube-kyle-long-arrested-threatened-violence-account-video-taken-down. Accessed 4 Sep 2019

  • Stephens S (2018) Have you ever regretted sharing a video of your child/children on youtube or social media? if so, why? https://rb.gy/08263v. Accessed 10 Sep 2019

  • Sureka A, Kumaraguru P, Goyal A, Chhabra S (2010) Mining youtube to discover extremist videos, users and hidden communities. In: Asia information retrieval symposium. Springer, pp 13–24

  • Szabo G, Huberman BA (2008) Predicting the popularity of online content. Available at SSRN 1295610

  • Tate C (2018) A quarter of teens regret posting a video on social media. https://rb.gy/8tbk1h. Accessed 10 Sep 2019

  • Thelwall M, Sud P, Vis F (2012) Commenting on youtube videos: from guatemalan rock to el big bang. J Am Soc Inform Sci Technol 63(3):616–629

    Article  Google Scholar 

  • Tinati R, Madaan A, Hall W (2017) Instacan: examining deleted content on instagram. In: Proceedings of the 2017 ACM on web science conference. ACM, pp 267–271

  • Trzciński T, Rokita P (2017) Predicting popularity of online videos using support vector regression. IEEE Trans Multimed 19(11):2561–2570

    Article  Google Scholar 

  • Tufekci Z (2012) Facebook, youth and privacy in networked publics. In: Sixth international AAAI conference on weblogs and social media

  • Walther JB, DeAndrea D, Kim J, Anthony JC (2010) The influence of online comments on perceptions of antimarijuana public service announcements on youtube. Hum Commun Res 36(4):469–492

    Article  Google Scholar 

  • Wang Y, Norcie G, Komanduri S, Acquisti A, Leon PG, Cranor LF (2011) I regretted the minute i pressed share: A qualitative study of regrets on facebook. In: Proceedings of the seventh symposium on usable privacy and security. ACM, p 10

  • Williams RL, Cothrel J (2000) Four smart ways to run online communities. MIT Sloan Manag Rev 41(4):81

    Google Scholar 

  • Wu S, Rizoiu MA, Xie L (2018) Beyond views: Measuring and predicting engagement in online videos. In: Twelfth international AAAI conference on web and social media

  • Xu JM, Burchfiel B, Zhu X, Bellmore A (2013) An examination of regret in bullying tweets. In: Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 697–702

  • YouTube (2019) Youtube in numbers. https://www.youtube.com/intl/en-GB/about/press. Accessed 9 Dec 2019

  • Yu H, Xie L, Sanner S (2015) The lifecyle of a youtube video: phases, content and popularity. In: Ninth international AAAI conference on web and social media

  • Zhou L, Wang W, Chen K (2016) Tweet properly: Analyzing deleted tweets to understand and identify regrettable ones. In: Proceedings of the 25th international conference on world wide web, international world wide web conferences steering committee, pp 603–612

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Kurdi, M., Albadi, N. & Mishra, S. “Think before you upload”: an in-depth analysis of unavailable videos on YouTube. Soc. Netw. Anal. Min. 11, 48 (2021). https://doi.org/10.1007/s13278-021-00755-x

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