Monetization of algorithmic media in the context of behavioral economics
Abstract
The analysis of video content production from the perspective of profit generation raises the issue of algorithmic media that rely on the attention economy. At its core lies the management of human behavior through algorithms for profit. By considering the concept of "attention" as a category of audience interaction with media, the parameters of attention control are demonstrated. A hypothesis is substantiated, according to which the limited rationality of modern humans has delegated its choice to algorithms. Physiological and cognitive barriers have become a media resource that, through codes, transforms human choice into the monetization of its own business. The theoretical analysis of the study is built on the model of H. Simon and D. Broadbent, demonstrating the principle of filtering information flows within a system with limited bandwidth. This also proves the limited mechanics of human decision-making under the pressure of a large amount of information. The practical component of the study consists of analyzing modern literature on algorithmic media and creating a classification of monetization parameters. It is emphasized that the modern business model of the media industry does not operate on a linear principle of monetization, where classical audience distribution guaranteed advertising investments. The work examines platforms such as YouTube, Meta, Douyin, TikTok, Pinterest, and Netflix, which develop according to the principle of attention distribution and exercise control over the automated behavior of their audiences. Four strategies for controlling the behavior of media product consumers are identified: the quantitative-temporal dimension, engagement, digital content tags, and the algorithmic logic of the illusion of choice. It is concluded that passive content consumption and critical perception of information are being replaced by managed automated pressure, where the viewer's attention becomes a convertible currency that ensures monetization.
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References
Meta. (2025, April 15). Cracking down on spammy content on Facebook. Retrieved March 2, 2026, from https://about.fb.com/news/2025/04/cracking-down-spammy-content-facebook/
Ferris, G. R., Fedor, D. B., & King, T. R. (1994). A political conceptualization of managerial behavior. Human Resource Management Review, 4(1), 1–34. https://doi.org/10.1016/1053-4822(94)90002-7
Moritz, J. M., et al. (2026). A meta-analysis on reactions to algorithmic decision-making in human resource management. Human Resource Management Review, 36(2), Article 101135. https://doi.org/10.1016/j.hrmr.2026.101135
Dibb, S., et al. (2021). Whose rationality? Muddling through the messy emotional reality of financial decision-making. Journal of Business Research, 131, 826–838. https://doi.org/10.1016/j.jbusres.2020.10.041
Simon, H. A. (1996). Models of my life. Cambridge, MA: MIT Press.
Broadbent, D. E. (1957). A mechanical model for human attention and immediate memory. Psychological Review, 64(3), 205–215.
Villi, M., & Picard, R. G. (2025). Transformation and innovation of media business models. In Making media (pp. 121–131). Routledge. https://doi.org/10.1017/9789048540150.009
Simon, H. A. (1982). Models of bounded rationality. Vol. 1: Economic analysis and public policy (pp. 161–176). Cambridge, MA: MIT Press.
Min, S. J. (2019). From algorithmic disengagement to algorithmic activism: Charting social media users’ responses to news filtering algorithms. Telematics and Informatics, 43, Article 101251. https://doi.org/10.1016/j.tele.2019.101251
Dekker, C. A., Baumgartner, S. E., & Sumter, S. R. (2025). For you vs. for everyone: The effectiveness of algorithmic personalization in driving social media engagement. Telematics and Informatics, 101, Article 102300. https://doi.org/10.1016/j.tele.2025.102300
Leonardi, P. M., & Vaast, E. (2017). Social media and their affordances for organizing: A review and agenda for research. Academy of Management Annals, 11(1), 150–188. https://doi.org/10.5465/annals.2015.0144
Verma, S., & Sheel, A. (2022). Blockchain for government organizations: Past, present and future. Journal of Global Operations and Strategic Sourcing, 15(3), 406–430. https://doi.org/10.1108/JGOSS-08-2021-0063
Tassi, P. (2018). Media: From the contact economy to the attention economy. International Journal of Arts Management, 21(1), 49–59. Retrieved from http://www.jstor.org/stable/44989736
Eom, S., Park, J., Choi, E., Park, J., & Kim, S. (2025). How do users contribute to YouTube channels’ revenue? An empirical analysis of Korean beauty channels. Computers in Human Behavior, 172, Article 108741. https://doi.org/10.1016/j.chb.2025.108741
Liang, M. (2022). The end of social media? How data attraction model in the algorithmic media reshapes the attention economy. Media, Culture & Society, 44(6), 1110–1131. https://doi.org/10.1177/01634437221077168
Gaw, F. (2022). Algorithmic logics and the construction of cultural taste of the Netflix Recommender System. Media, Culture & Society, 44(4), 706–725. https://doi.org/10.1177/01634437211053767
Thaler, R. H., & Sunstein, C. R. (2023). Libertarian paternalism. In Research handbook on nudges and society (pp. 10–16). Edward Elgar Publishing. https://doi.org/10.1257/000282803321947001
Gaw F. (2022b). Algorithms and the construction of cultural taste of the Netflix Recommender System: video; iNOVA Media Lab. Retrieved from https://www.youtube.com/watch?v=BUifBpxrcCI&t=6s
Aylsworth, T., & Castro, C. (2024). Kantian ethics and the attention economy: Duty and distraction. Cham, Switzerland: Springer Nature. https://doi.org/10.1007/978-3-031-45638-1
State Intellectual Property Office of the P.R.C. (2017). Chinese Patent No. CN106534990A. Retrieved from https://worldwide.espacenet.com/patent/search/family/058341525 /publication/CN106534990A?q=YouTube
Korean Intellectual Property Office. (2024). Korean Patent No. KR20240086613A. Retrieved from https://worldwide.espacenet.com/patent/search/family/091670868/publication/KR20240086613A?q=pn%3DKR20240086613A
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