research-article

Public Access

Paying Attention to the Algorithm Behind the Curtain: Bringing Transparency to YouTube's Demonetization Algorithms

Authors:

Katherine A. Keith,

Ethan Zuckerman,

Narseo Vallina-Rodriguez,

Brendan O'Connor,

Rishab NithyanandAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue CSCW2

Article No.: 318, Pages 1 - 31

https://doi.org/10.1145/3555209

Published: 11 November 2022 Publication History

Abstract

YouTube has long been a top-choice destination for independent video content creators to share their work. A large part of YouTube's appeal is owed to its practice of sharing advertising revenue with qualifying content creators through the YouTube Partner Program (YPP). In recent years, changes to the monetization policies and the introduction of algorithmic systems for making monetization decisions have been a source of controversy and tension between content creators and the platform. There have been numerous accusations suggesting that the underlying monetization algorithms engage in preferential treatment of larger channels and effectively censor minority voices by demonetizing their content.

In this paper, we conduct a measurement of the YouTube monetization algorithms. We begin by measuring the incidence rates of different monetization decisions and the time taken to reach them. Next, we analyze the relationships between video content, channel popularity and these decisions. Finally, we explore the relationship between demonetization and a channel's view growth rate. Taken all together, our work suggests that demonetization after a video is publicly listed is not a common occurrence, the characteristics of the process are associated with channel size and (in unexplainable ways) video topic, and demonetization appears to have a harsh influence on the growth rate of smaller channels. We also highlight the challenges associated with conducting large-scale algorithm audits such as ours and make an argument for more transparency in algorithmic decision-making.

References

[1]

2018. The forever war of PewDiePie, YouTube's biggest creator. https://www.washingtonpost.com/technology/2018/12/20/forever-war-pewdiepie-youtubes-biggest-creator/

[2]

2018. Top YouTube content categories by share of uploads 2018: Statista. https://www.statista.com/statistics/1026914/global-distribution-youtube-video-content-by-category/

[3]

2019. Divino Group LLC v. Google LLC. https://www.classaction.org/media/divino-group-et-al-v-google-llc-et-al.pdf

[4]

2020. 47 U.S. Code §230 - Protection for private blocking and screening of offensive material | U.S. Code | US Law | LII / Legal Information Institute. https://www.law.cornell.edu/uscode/text/47/230

[5]

2020. The Code of conduct on countering illegal hate speech online. https://ec.europa.eu/commission/presscorner/detail/en/qanda_20_1135

[6]

2020. Wayback Machine: Advertiser-friendly content guidelines. https://web.archive.org/web/20200811083202/https://support.google.com/youtube/answer/6162278?hl=en&ref_topic=9153642

[7]

2020. WayBack Machine: Request human review of videos marked "Not suitable for most advertisers" - YouTube Help. https://web.archive.org/web/20201020204246/https://support.google.com/youtube/answer/7083671?hl=en&ref_topic=1115890

[8]

2020. Wayback Machine: Uploading videos to monetize with ads. https://web.archive.org/web/20200828082433/https://support.google.com/youtube/answer/75619389153642

[9]

2020. Wayback Machine: YouTube channel monetization policies - YouTube Help. https://web.archive.org/web/20200724232124/ https://support.google.com/youtube/answer/1311392

[10]

2020. WayBack Machine: YouTube Community Guidelines & Policies - How YouTube Works. https://web.archive.org/web/20200811044808/https://www.youtube.com/howyoutubeworks/policies/community-guidelines/#enforcing-policies

[11]

2020. Wayback Machine: YouTube Partner Program overview & eligibility - YouTube Help. https://web.archive.org/web/20200725004834/https://support.google.com/youtube/answer/72851?hl=en

[12]

2020. Wayback Machine: YouTube Rules and Policies: Community Guidelines. https://web.archive.org/web/20200725011550/https://www.youtube.com/howyoutubeworks/policies/community-guidelines/

[13]

2020. Wayback Machine: YouTube Rules and Policies: Monetization Policies. https://web.archive.org/web/20200722015509/https://www.youtube.com/howyoutubeworks/policies/monetization-policies/

[14]

2021. BRIEF OF AMICI CURIAE KYRATSO KARAHALIOS, ALAN MISLOVE, CHRISTIAN W. SANDVIG, CHRISTOPHER WILSON, FIRST LOOK MEDIA WORKS, THE AMERICAN CIVIL LIBERTIES UNION, THE AMERICAN CIVIL LIBERTIES UNION OF THE DISTRICT OF COLUMBIA, UPTURN, AND THE KNIGHT FIRST AMENDMENT INSTITUTE IN SUPPORT OF PETITIONER. https://cbw.sh/static/pdf/amicus_aclu.pdf

[15]

2021. Upcoming and recent ad guideline updates - YouTube Help. https://support.google.com/youtube/answer/9725604?hl=en&ref_topic=9153642

[16]

2021. Van Buren is a Victory Against Overbroad Interpretations of the CFAA, and Protects Security Researchers. https://www.eff.org/deeplinks/2021/06/van-buren-victory-against-overbroad-interpretations-cfaa-protects-security

[17]

2021. Van Buren vs. United States. https://www.supremecourt.gov/opinions/20pdf/19--783_k53l.pdf

[18]

2021. YouTube channel monetization policies: How we enforce YouTube monetization policies - YouTube Help. https://support.google.com/youtube/answer/1311392?visit_id=637535273191990793--852634117&rd=1#zippy=%2Cfollow-the-youtube-community-guidelines%2Cturn-off-ads-from-your-content%2Csuspend-your-participation-in-the-youtube-partner-program%2Csuspend-or-even-terminate-your-youtube-channel%2Chow-well-inform-you-of-actions-that-affect-your-monetization

[19]

2021. YouTube Data API: Google Developers. https://developers.google.com/youtube/v3

[20]

2021. YouTube Partner Program overview & eligibility - YouTube Help. https://support.google.com/youtube/answer/72851?hl=en#zippy=%2Cim-no-longer-in-ypp-or-i-was-never-in-the-program-and-im-seeing-ads-on-my-videos-am-i-earning-revenue-from-those-ads%2Cwhat-if-i-dont-meet-the-program-threshold

[21]

Alberto Abadie. 2005. Semiparametric difference-in-differences estimators. The Review of Economic Studies 72, 1 (2005), 1--19.

[22]

Ali Alkhatib. 2021. To Live in Their Utopia: Why Algorithmic Systems Create Absurd Outcomes. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). ACM, New York, NY, USA.

Digital Library

[23]

Ali Alkhatib and Michael Bernstein. 2019. Street-level algorithms: A theory at the gaps between policy and decisions. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1--13.

Digital Library

[24]

Mike Ananny. 2018. Networked press freedom: Creating infrastructures for a public right to hear. MIT Press.

[25]

Mark Andrejevic. 2009. Exploiting YouTube: Contradictions of user-generated labor. The YouTube Reader 413 (2009), 36.

[26]

Joshua D Angrist and Jörn-Steffen Pischke. 2008. Mostly harmless econometrics: An empiricist's companion. Princeton university press.

[27]

Camila Souza Araújo, Gabriel Magno, Wagner Meira Jr., Virgílio A. F. Almeida, Pedro Hartung, and Danilo Doneda. 2017. Characterizing Videos, Audience and Advertising in Youtube Channels for Kids. In Social Informatics - 9th International Conference, SocInfo 2017, Oxford, UK, September 13--15, 2017, Proceedings, Part I (Lecture Notes in Computer Science, Vol. 10539), Giovanni Luca Ciampaglia, Afra J. Mashhadi, and Taha Yasseri (Eds.). Springer, 341--359. https://doi.org/10.1007/978--3--319--67217--5_21

[28]

David Bamman, Brendan O'Connor, and Noah A. Smith. 2012. Censorship and Deletion Practices in Chinese Social Media. First Monday 17, 3 (2012).

[29]

Jason Baumgartner, Savvas Zannettou, Brian Keegan, Megan Squire, and Jeremy Blackburn. 2020. The pushshift reddit dataset. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 14. 830--839.

[30]

David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. the Journal of machine Learning research 3 (2003), 993--1022.

[31]

Abram Brown. 2020. What Is Section 230-And Why Does Trump Want To Change It? https://www.forbes.com/sites/abrambrown/2020/05/28/what-is-section-230-and-why-does-trump-want-to-change-it/'sh=552684b2389d

[32]

Robyn Caplan and Tarleton Gillespie. 2020. Tiered Governance and Demonetization: The Shifting Terms of Labor and Compensation in the Platform Economy. Social Media Society 6, 2 (2020), 2056305120936636. https://doi.org/10.1177/2056305120936636 arXiv:https://doi.org/10.1177/2056305120936636

[33]

Eshwar Chandrasekharan, Umashanthi Pavalanathan, Anirudh Srinivasan, Adam Glynn, Jacob Eisenstein, and Eric Gilbert. 2017. You can't stay here: The efficacy of reddit's 2015 ban examined through hate speech. Proceedings of the ACM on Human-Computer Interaction 1, CSCW (2017), 1--22.

Digital Library

[34]

Le Chen, Alan Mislove, and Christo Wilson. 2015. Peeking Beneath the Hood of Uber. In Proceedings of the Internet Measurement Conference (IMC 2015). Tokyo, Japan.

Digital Library

[35]

Le Chen, Alan Mislove, and Christo Wilson. 2016. An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace. In Proceedings of the 25th International World Wide Web Conference (WWW 2016). Montreal, Canada.

Digital Library

[36]

Justin Cheng, Michael Bernstein, Cristian Danescu-Niculescu-Mizil, and Jure Leskovec. 2017. Anyone can become a troll: Causes of trolling behavior in online discussions. In Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing. 1217--1230.

Digital Library

[37]

Thomas J DiCiccio, Bradley Efron, et al. 1996. Bootstrap confidence intervals. Statistical science 11, 3 (1996), 189--228.

[38]

Lindsay Dodgson. 2019. YouTubers have identified a long list of words that immediately get videos demonetized, and they include 'gay' and 'lesbian' but not 'straight' or 'heterosexual'. https://www.insider.com/youtubers-identify-title-words-that-get-videos-demonetized-experiment-2019--10

[39]

Elizabeth Dwoskin. 2019. YouTube's arbitrary standards: Stars keep making money even after breaking the rules. The Washington Post (2019).

[40]

Yelena Dzhanova. 2019. Forget law school, these kids want to be a YouTube star. https://www.cnbc.com/2019/08/02/forget-law-school-these-kids-want-to-be-a-youtube-star.html

[41]

Bradley Efron. 1987. Better bootstrap confidence intervals. Journal of the American statistical Association 82, 397 (1987), 171--185.

[42]

Jacob Eisenstein. 2013. What to do about bad language on the internet. In Proceedings of the 2013 conference of the North American Chapter of the association for computational linguistics: Human language technologies. 359--369.

[43]

Jacob Eisenstein. 2019. Introduction to Natural Language Processing. MIT Press.

[44]

Megan Graham. 2020. YouTube will put ads on non-partner videos but won't pay the creators. https://www.cnbc.com/2020/11/19/youtube-will-put-ads-on-non-partner-videos-but-wont-pay-the-creators.html

[45]

Mary L Gray and Siddharth Suri. 2019. Ghost work: How to stop Silicon Valley from building a new global underclass. Eamon Dolan Books.

[46]

Thomas L Griffiths and Mark Steyvers. 2004. Finding scientific topics. Proceedings of the National academy of Sciences 101, suppl 1 (2004), 5228--5235.

[47]

Justin Grimmer and Brandon M Stewart. 2013. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis 21, 3 (2013), 267--297.

[48]

Aniko Hannak, Piotr Sapiezynski, Arash Molavi Kakhki, Balachander Krishnamurthy, David Lazer, Alan Mislove, and Christo Wilson. 2013. Measuring Personalization of Web Search. In Proceedings of the 22nd International World Wide Web Conference (WWW 2013). Rio de Janeiro, Brazil.

Digital Library

[49]

Miguel A Hernán and James M Robins. 2020. Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.

[50]

Margaret Holland. 2016. How YouTube developed into a successful platform for user-generated content. Elon Journal of Undergraduate Research in Communications 7, 1 (2016).

[51]

Eslam Hussein, Prerna Juneja, and Tanushree Mitra. 2020. Measuring misinformation in video search platforms: An audit study on YouTube. Proceedings of the ACM on Human-Computer Interaction 4, CSCW1 (2020), 1--27.

Digital Library

[52]

Knight Institute. 2021. Researchers, NYU, Knight Institute Condemn Facebook's Effort to Squelch Independent Research about Misinformation. https://knightcolumbia.org/content/researchers-nyu-knight-institute-condemn-facebooks-effort-to-squelch-independent-research-about-misinformation

[53]

Shan Jiang, Ronald E. Robertson, and Christo Wilson. 2019. Bias Misperceived:The Role of Partisanship and Misinformation in YouTube Comment Moderation. Proceedings of the International AAAI Conference on Web and Social Media 13, 01 (Jul. 2019), 278--289. https://ojs.aaai.org/index.php/ICWSM/article/view/3229

[54]

Shan Jiang, Ronald E. Robertson, and Christo Wilson. 2020. Reasoning about Political Bias in Content Moderation. Proceedings of the AAAI Conference on Artificial Intelligence 34, 09 (Apr. 2020), 13669--13672. https://doi.org/10.1609/aaai.v34i09.7117

[55]

Shan Jiang and Christo Wilson. 2018. Linguistic signals under misinformation and fact-checking: Evidence from user comments on social media. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1--23.

Digital Library

[56]

Daniel Jurafsky and James H. Martin. 2009. Speech and Language Processing (2nd Edition). Prentice-Hall, Inc.

Digital Library

[57]

Jacob Kastrenakes. 2020. YouTube will rely more on AI moderation while human reviewers can't come to the office. https://www.theverge.com/2020/3/16/21182011/youtube-ai-moderation-coronavirus-video-removal-increase-warning

[58]

Katherine Keith, David Jensen, and Brendan O'Connor. 2020. Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 5332--5344.

[59]

Martin Kenney and John Zysman. 2016. The Rise of the Platform Economy. Issues in science and technology 32 (03 2016), 61--69.

[60]

Emre Kiciman, Scott Counts, and Melissa Gasser. 2018. Using longitudinal social media analysis to understand the effects of early college alcohol use. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 12.

[61]

Sangeet Kumar. 2019. The algorithmic dance: YouTube's Adpocalypse and the gatekeeping of cultural content on digital platforms. Internet Policy Review 8, 2 (2019), 1--21. https://doi.org/10.14763/2019.2.1417

[62]

Liz Lavaveshkul. 2012. How to achieve 15 minutes (or more) of fame through youtube. J. Int'l Com. L. & Tech. 7 (2012), 370.

[63]

Paige Leskin. 2019. American Kids Want to Be YouTube Stars: Survey. https://www.businessinsider.com/american-kids-youtube-star-astronauts-survey-2019--7

[64]

Edward Loper and Steven Bird. 2002. Nltk: The natural language toolkit. arXiv preprint cs/0205028 (2002).

Digital Library

[65]

Rebecca MacKinnon. 2009. China's censorship 2.0: How companies censor bloggers. First Monday 14, 2 (February 2009).

[66]

Andrew Kachites McCallum. 2002. MALLET: A Machine Learning for Language Toolkit. (2002). http://mallet.cs.umass.edu.

[67]

McKeown. 2020. Prager University v. Google LLC: Opinion from Judge McKeown. https://cdn.ca9.uscourts.gov/datastore/opinions/2020/02/26/18--15712.pdf

[68]

Toby Miller. 2009. Cybertarians of the world unite: You have nothing to lose but your tubes! National Library of Sweden.

[69]

Stephen L Morgan and Christopher Winship. 2015. Counterfactuals and causal inference. Cambridge University Press.

[70]

Dong Nguyen, Maria Liakata, Simon DeDeo, Jacob Eisenstein, David Mimno, Rebekah Tromble, and Jane Winters. 2020. How we do things with words: Analyzing text as social and cultural data. Frontiers in Artificial Intelligence 3 (2020), 62.

[71]

David B Nieborg and Thomas Poell. 2018. The platformization of cultural production: Theorizing the contingent cultural commodity. New media & society 20, 11 (2018), 4275--4292.

[72]

Brendan O'Connor, David Bamman, and Noah A Smith. 2011. Computational text analysis for social science: Model assumptions and complexity. In Second workshop on comptuational social science and the wisdom of crowds (NIPS 2011). Citeseer.

[73]

Kostantinos Papadamou, Antonis Papasavva, Savvas Zannettou, Jeremy Blackburn, Nicolas Kourtellis, Ilias Leontiadis, Gianluca Stringhini, and Michael Sirivianos. 2020. Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children. Proceedings of the International AAAI Conference on Web and Social Media (2020).

[74]

Kostantinos Papadamou, Savvas Zannettou, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and Michael Sirivianos. 2021. Understanding the Incel Community on YouTube. arXiv:2001.08293 [cs.CY]

[75]

Judea Pearl. 2009. Causality. Cambridge university press.

[76]

Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532--1543.

[77]

Radim Rehurek and Petr Sojka. 2010. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. ELRA, Valletta, Malta, 45--50.

[78]

Manoel Horta Ribeiro, Raphael Ottoni, Robert West, Virgílio A. F. Almeida, and Wagner Meira Jr. 2020. Auditing radicalization pathways on YouTube. In FAT* '20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27--30, 2020, Mireille Hildebrandt, Carlos Castillo, Elisa Celis, Salvatore Ruggieri, Linnet Taylor, and Gabriela Zanfir-Fortuna (Eds.). ACM, 131--141. https://doi.org/10.1145/3351095.3372879

Digital Library

[79]

Caitlin M Rivers and Bryan L Lewis. 2014. Ethical research standards in a world of big data. F1000Research 3 (2014).

[80]

Margaret E Roberts. 2018. Censored: Distraction and Diversion Inside China's Great Firewall. (2018).

[81]

Margaret E Roberts, Brandon M Stewart, and Richard A Nielsen. 2020. Adjusting for confounding with text matching. American Journal of Political Science 64, 4 (2020), 887--903.

[82]

Margaret E Roberts, Brandon M Stewart, Dustin Tingley, Christopher Lucas, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson, and David G Rand. 2014. Structural topic models for open-ended survey responses. American Journal of Political Science 58, 4 (2014), 1064--1082.

[83]

Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort. 2014. Auditing algorithms: Research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry (2014).

[84]

Laura Sehl and Philippe Ross. 2020. Demonetization on YouTube and the Visibility of News Produced by Non-Mainstream News Commentators.

[85]

Erin Shaak. 2020. Racism for Profit: Black Content Creators Sue YouTube Over Discriminatory Access Restrictions. https://www.classaction.org/blog/racism-for-profit-black-content-creators-sue-youtube-over-discriminatory-access-restrictions

[86]

SocialBlade. 2021. SocialBlade: Analytics Made Easy. https://socialblade.com

[87]

Stephen Stanford. 2018. YouTube and the Adpocalypse: How Have The New YouTube Advertising Friendly Guidelines Shaped Creator Participation and Audience Engagement?

[88]

Soroush Vosoughi, Deb Roy, and Sinan Aral. 2018. The spread of true and false news online. Science 359, 6380 (2018), 1146--1151.

[89]

Geoff Weiss. 2017. Creators Cry Foul After YouTube Demonetizes Casey Neistat's LoveArmyLasVegas Video - Tubefilter. https://www.tubefilter.com/2017/10/06/youtube-demonetization-casey-neistats-love-army-las-vegas/

[90]

Wayne W Wilkinson and Stephen D Berry. 2020. Together they are Troy and Chase: Who supports demonetization of gay content on YouTube? Psychology of Popular Media 9, 2 (2020), 224.

[91]

Rolf Winkler, Jack Nicas, and Ben Fritz. 2016. PewDiePie Says WSJ Took Anti-Semitic Content Out of Context. https://www.wsj.com/articles/pewdiepie-says-wsj-took-anti-semitic-content-out-of-context-1487278375

[92]

Eva Yiwei Wu, Emily Pedersen, and Niloufar Salehi. 2019. Agent, Gatekeeper, Drug Dealer: How Content Creators Craft Algorithmic Personas. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 219 (Nov. 2019), 27 pages. https://doi.org/10.1145/3359321

Digital Library

[93]

Leon Yin and Aaron Sankin. 2021. Google Has a Secret Blocklist that Hides YouTube Hate Videos from Advertisers-But It's Full of Holes -- The Markup. https://themarkup.org/google-the-giant/2021/04/08/google-youtube-hate-videos-ad-keywords-blocklist-failures

[94]

YouTube. 2020. Protecting our extended workforce and the community. https://blog.youtube/news-and-events/protecting-our-extended-workforce-and/

[95]

YouTube. 2021. Trending on YouTube. https://support.google.com/youtube/answer/7239739?hl=en

[96]

YouTube. 2021. YouTube for Press: YouTube in numbers. https://www.youtube.com/intl/en-GB/about/press/

[97]

YouTube. 2021. YouTube Partner Program overview & eligibility - YouTube Help. https://support.google.com/youtube/answer/72851?hl=en

[98]

Savvas Zannettou, Sotirios Chatzis, Kostantinos Papadamou, and Michael Sirivianos. 2018. The Good, the Bad and the Bait: Detecting and Characterizing Clickbait on YouTube. In 2018 IEEE Security and Privacy Workshops (SPW). 63--69. https://doi.org/10.1109/SPW.2018.00018

Cited By

Ditlhokwa G(2024)Unlocking Monetization Potential in the Age of YouTube Algorithmic Bias: An Analysis of Botswana FilmmakingThe Future of Television and Video Industry10.5772/intechopen.113306Online publication date: 12-Jun-2024
https://doi.org/10.5772/intechopen.113306
Taylor JSimpson ETran ABrubaker JFox SZhu H(2024)Cruising Queer HCI on the DL: A Literature Review of LGBTQ+ People in HCIProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642494(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642494
Singhal MLing CPaudel PThota PKumarswamy NStringhini GNilizadeh S(2023)SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00056(868-895)Online publication date: Jul-2023
https://doi.org/10.1109/EuroSP57164.2023.00056

Index Terms

Paying Attention to the Algorithm Behind the Curtain: Bringing Transparency to YouTube's Demonetization Algorithms
1. Human-centered computing
  1. Human computer interaction (HCI)
2. Information systems
  1. Information systems applications
    1. Decision support systems
  2. World Wide Web
    1. Web mining

Recommendations

Characterizing Alternative Monetization Strategies on YouTube
CSCW

One of the key emerging roles of the YouTube platform is providing creators the ability to generate revenue from their content and interactions. Alongside tools provided directly by the platform, such as revenue-sharing from advertising, creators co-opt ...
"Give Everybody [..] a Little Bit More Equity": Content Creator Perspectives and Responses to the Algorithmic Demonetization of Content Associated with Disadvantaged Groups
CSCW

Algorithmic systems help manage the governance of digital platforms featuring user-generated content, including how money is distributed to creators from the profits a platform earns from advertising on this content. However, creators producing content ...
YouTube Monetization and Censorship by Proxy: A Machine Learning Prospective
Abstract
As consumption of digital content has climbed, so has censorship of the content. The censorship has only increased with companies more sensitive to the type of content that they tie their advertising to on digital platforms. Demonetization of ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue CSCW2

CSCW

November 2022

8205 pages

EISSN:2573-0142

DOI:10.1145/3571154

Editor:
Jeff Nichols
Google

Issue’s Table of Contents

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 November 2022

Published in PACMHCI Volume 6, Issue CSCW2

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
797
Total Downloads

Downloads (Last 12 months)559
Downloads (Last 6 weeks)55

Reflects downloads up to 26 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ditlhokwa G(2024)Unlocking Monetization Potential in the Age of YouTube Algorithmic Bias: An Analysis of Botswana FilmmakingThe Future of Television and Video Industry10.5772/intechopen.113306Online publication date: 12-Jun-2024
https://doi.org/10.5772/intechopen.113306
Taylor JSimpson ETran ABrubaker JFox SZhu H(2024)Cruising Queer HCI on the DL: A Literature Review of LGBTQ+ People in HCIProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642494(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642494
Singhal MLing CPaudel PThota PKumarswamy NStringhini GNilizadeh S(2023)SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice2023 IEEE 8th European Symposium on Security and Privacy (EuroS&P)10.1109/EuroSP57164.2023.00056(868-895)Online publication date: Jul-2023
https://doi.org/10.1109/EuroSP57164.2023.00056

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Media

Figures

Other

Tables

View Issue’s Table of Contents