Cited by 36

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Characterizing Alternative Monetization Strategies on YouTube

Hua, Yiqing, Horta Ribeiro, Manoel, Ristenpart, Thomas, West, Robert, Naaman, Mor·2022·Proceedings of the ACM on Human-Computer Interaction

Abstract

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 the platform to use a variety of off-platform monetization opportunities. In this work, we focus on studying and characterizing these alternative monetization strategies. Leveraging a large longitudinal YouTube dataset of popular creators, we develop a taxonomy of alternative monetization strategies and a simple methodology to detect their usage automatically. We then proceed to characterize the adoption of these strategies. First, we find that the use of external monetization is expansive and increasingly prevalent, used in 18% of all videos, with 61% of channels using one such strategy at least once. Second, we show that the adoption of these strategies varies substantially among channels of different kinds and popularity, and that channels that establish these alternative revenue streams often become more productive on the platform. Lastly, we investigate how potentially problematic channels -- those that produce Alt-lite, Alt-right, and Manosphere content -- leverage alternative monetization strategies, finding that they employ a more diverse set of such strategies significantly more often than a carefully chosen comparison set of channels. This finding complicates YouTube's role as a gatekeeper, since the practice of excluding policy-violating content from its native on-platform monetization may not be effective. Overall, this work provides an important step toward broadening the understanding of the monetary incentives behind content creation on YouTube.

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Metadata

Authors
Hua, Y., Horta Ribeiro, M., Ristenpart, T., West, R., & Naaman, M.
Affiliations
  • Cornell Tech, New York, NY, USA
  • EPFL, Lausanne, Switzerland
Venue
Proceedings of the ACM on Human-Computer Interaction
Published
2022-11-07
Volume
6
Issue
CSCW2
Pages
1-30
ISSN
2573-0142
Cited by
36
References
3
Language
en
Funders
National Science Foundation (aTC-2120651)

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