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Research on Stakeholder Negotiation and Imitative Platform Expression of High-energy Vlogs from the Perspective of Algorithmic Visibility

Ruihan Geng
pp.212-217 DOI: 10.62051/00vzfg90 August 26, 2026
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Abstract: To clarify the generation logic of short video homogenization driven by algorithm distribution, this article uses algorithm visibility as the analytical framework to explore the intrinsic relationship between the platform, creator, and user tripartite negotiation mechanism and the large-scale imitation production of high-energy vlogs. This paper selects TikTok, Bilibili and Xiaohongshu as research samples and carries out empirical analysis based on statistical data from China Audio-Video Association, QuestMobile and NewRank. Actual test data shows that the completion rate of high-energy emotional vlogs reaches 63.4%, which is 25.3 percentage points higher than conventional documentary vlogs. The data advantage has made this type of content the main replication template in the industry. The hierarchical traffic distribution mechanism promotes a long-term interaction among three parties: the platform regulates content development by adjusting algorithm weight parameters, creators obtain exposure by imitating existing works, and user interaction data continuously drives the iteration of recommendation models. Imitative works raise the costs of trial and error for emerging creators. The Matthew effect in traffic distribution also worsens content homogenization across vlog content segments. This paper puts forward corresponding solutions from four perspectives: algorithm optimization, vertical content creation, creator competency development and industry regulation, to drive the sound development of this segment.
Keywords: Algorithm visibility; Platform Stakeholder Negotiation; High-energy Vlog; Imitative expression; Short video content ecosystem.
APA Citation: Ruihan Geng (2026). Research on Stakeholder Negotiation and Imitative Platform Expression of High-energy Vlogs from the Perspective of Algorithmic Visibility. Transactions on Social Science, Education and Humanities Research, 17(1), 212-217. https://doi.org/10.62051/00vzfg90

References

  1. Zhou, R. (2024). Understanding the impact of TikTok’s recommendation algorithm on user engagement. International Journal of Computer Science and Information Technology, 3(2), 201 208.
  2. Huang, Y., & Ye, W. (2024). “Traffic rewards”, “algorithmic visibility”, and “advertiser satisfaction”: How Chinese short video platforms cultivate creators in stages. Convergence, 30(1), 659 682. https://doi.org/10.1177/13548585231197733
  3. Zeng, J., & Kaye, D. B. V. (2022). From content moderation to visibility moderation: A case study of platform governance on TikTok. Policy & Internet, 14(1), 79 95. https://doi.org/10.1002/poi3.281
  4. Gong, X., Feng, Q., Zhang, Y., Qin, J., Ding, W., Li, B., & Gai, K. (2022, October). Real time short video recommendation on mobile devices. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 3103 3112). ACM. https://doi.org/10.1145/3511808.3557065
  5. Piao, J., Liu, J., Zhang, F., Su, J., & Li, Y. (2023). Human AI adaptive dynamics drives the emergence of information cocoons. Nature Machine Intelligence, 5(11), 1214 1224. https://doi.org/10.1038/s42256 023 00730 6
  6. Duffy, B. E., & Meisner, C. (2023). Platform governance at the margins: Social media creators’ experiences with algorithmic (in) visibility. Media, Culture & Society, 45(2), 285 304. https://doi.org/10.1177/01634437221108567
  7. Gao, Y., Liu, F., & Gao, L. (2023). Echo chamber effects on short video platforms. Scientific Reports, 13(1), 6282. https://doi.org/10.1038/s41598 023 33396 6
  8. Wang, Y., Ma, W., Zhang, M., Liu, Y., & Ma, S. (2023). A survey on the fairness of recommender systems. ACM Transactions on Information Systems, 41(3), 1 43. https://doi.org/10.1145/3576043
  9. Christin, A. (2020). The ethnographer and the algorithm: Beyond the black box. Theory and Society, 49(5), 897 918. https://doi.org/10.1007/s11186 020 09367 6
  10. Choi, Y., Kang, E. J., Lee, M. K., & Kim, J. (2023, April). Creator friendly algorithms: Behaviors, challenges, and design opportunities in algorithmic platforms. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1 22). ACM. https://doi.org/10.1145/3544548.3581347
  11. Gorwa, R., Binns, R., & Katzenbach, C. (2020). Algorithmic content moderation: Technical and political challenges in the automation of platform governance. Big Data & Society, 7(1), 2053951719897945. https://doi.org/10.1177/2053951719897945