A Study on Users' Algorithmic Imagination and Counter-Domestication Behavior from the Perspective of Media Affordances: The Case of the "Momo" Phenomenon

Authors

  • Xuantong Wu School of Journalism and Communication, Communication University of Zhejiang, Zhejiang, 310018, China Author

Keywords:

media affordance, "momo" phenomenon, algorithmic counter-domestication, algorithmic visibility, algorithmic imagination

Abstract

This paper takes the "momo" phenomenon as a case study, examining users' algorithmic imagination and counter-domestication behaviors toward social platform algorithms through the three dimensions of media affordances: production affordance, social affordance, and mobile affordance. By analyzing the dissemination and underlying mechanisms of the "momo" phenomenon, it reveals how users leverage media affordances to resist and protect themselves against algorithmic constraints and potential risks. The study further explores the relationship between algorithmic visibility in platform society and users' algorithmic imagination reflected in this phenomenon, while discussing its implications for the future development of social platforms and user-algorithm interaction practices.

References

1. G. Appel, L. Grewal, R. Hadi, and A. T. Stephen, "The future of social media in marketing," Journal of the Academy of Marketing science, vol. 48, no. 1, pp. 79-95, 2020. doi: 10.1007/s11747-019-00695-1

2. H. J. Cheong, S. M. Baksh, and I. Ju, "Spiral of silence in an algorithm-driven social media content environment: Conceptual framework and research propositions," Kome, vol. 10, no. 1, pp. 32-46, 2022. doi: 10.17646/kome.75672.86

3. W. Liu, "Design and user behavior analysis of an English learning social platform based on digital entertainment content recommendation algorithm," Entertainment Computing, vol. 51, p. 100734, 2024. doi: 10.1016/j.entcom.2024.100734

4. J. Xu, "Analysis of social media algorithm recommendation system," Studies in Social Science & Humanities, vol. 1, no. 3, pp. 57-63, 2022. doi: 10.56397/sssh.2022.10.06

5. A. Gautier, A. Ittoo, and P. Van Cleynenbreugel, "AI algorithms, price discrimination and collusion: a technological, economic and legal perspective," European Journal of Law and Economics, vol. 50, no. 3, pp. 405-435, 2020. doi: 10.1007/s10657-020-09662-6

6. Y. Li, and J. Cao, "The Role of Recommendation Algorithm in Online Social Platforms," Procedia Computer Science, vol. 261, pp. 674-681, 2025. doi: 10.1016/j.procs.2025.04.320

7. Y. Zhou, "Being Momo: Algorithmic Imaginary and Resistance," 2024.

8. T. Bitzer, M. Wiener, and W. A. Cram, "Algorithmic transparency: Concepts, antecedents, and consequences-a review and research framework," Communications of the Association for Information Systems, vol. 52, no. 1, pp. 293-331, 2023.

9. J. C. Magalhães, and J. Yu, "Mediated visibility and recognition: A taxonomy," The new politics of visibility: Spaces, actors, practices and technologies in the visible, pp. 72-99, 2022.

10. C. Celis, and M. J. Schultz, "Notes on an Algorithmic Faculty of the Imagination," Anthropocenes-Human, Inhuman, Posthuman, vol. 2, no. 1, 2021. doi: 10.16997/ahip.1016

11. G. Chaudhary, "Unveiling the black box: Bringing algorithmic transparency to AI," Masaryk University Journal of Law and Technology, vol. 18, no. 1, pp. 93-122, 2024. doi: 10.5817/mujlt2024-1-4

12. U. Bhatt, J. Antorán, Y. Zhang, Q. V. Liao, P. Sattigeri, R. Fogliato, and A. Xiang, "Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty," In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, July, 2021, pp. 401-413. doi: 10.1145/3461702.3462571

13. L. Haddon, "Domestication and the media," The international encyclopedia of media effects, vol. 1, pp. 409-17, 2017.

14. Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef, "Recommendation systems: Algorithms, challenges, metrics, and business opportunities," applied sciences, vol. 10, no. 21, p. 7748, 2020. doi: 10.3390/app10217748

15. L. Zhang, Y. Lian, H. Wu, C. Song, and X. Yuan, "An Exploratory Study on Information Cocoon in Recommender Systems: L," Zhang et al. Data Science and Engineering, pp. 1-20, 2025.

Downloads

Published

2025-11-23

How to Cite

Wu, X. (2025). A Study on Users’ Algorithmic Imagination and Counter-Domestication Behavior from the Perspective of Media Affordances: The Case of the "Momo" Phenomenon. Simen Owen Academic Proceedings Series, 2, 91-103. https://simonowenpub.com/index.php/SOAPS/article/view/37