Analysis of the Homogeneity of Algorithm Recommendation-driven Content Creation on Short Video Platforms

Research Article
Open access

Analysis of the Homogeneity of Algorithm Recommendation-driven Content Creation on Short Video Platforms

Yixuan Qin 1*
  • 1 Faculty of Arts, Monash University, Melbourne, Australia    
  • *corresponding author yqin6058@gmail.com
CHR Vol.67
ISSN (Print): 2753-7072
ISSN (Online): 2753-7064
ISBN (Print): 978-1-80590-115-0
ISBN (Online): 978-1-80590-116-7

Abstract

Unlike traditional social platforms, short video platforms analyze users' interest preferences, viewing behavior, and interaction records in real time through big data and artificial intelligence algorithms, providing personalized content recommendations. While this customization improves user experience and platform engagement, it has also led to a certain degree of content convergence. This study explores how algorithms drive content style convergence on short video platforms and influence users' creative intentions and aesthetic preferences. The research highlights that while recommendation algorithms enhance user experience, they also contribute to the homogenization of content creation. Moreover, platform design and communication mechanisms play a crucial role in shaping public perceptions of aesthetics, entertainment, and social values. By analyzing platform algorithms and their underlying mechanisms, this research offers a theoretical foundation for improving short video platform design and reflects on the ethical responsibilities these platforms bear in shaping digital culture. However, the study has certain limitations, including the reliance on qualitative and platform-based analysis without extensive empirical user data or creator interviews. Future research could benefit from a mixed-methods approach that includes quantitative user engagement data and qualitative interviews with content creators and platform engineers.

Keywords:

Algorithmic recommendation systems, short video platforms, content convergence, algorithmic aesthetics

Qin,Y. (2025). Analysis of the Homogeneity of Algorithm Recommendation-driven Content Creation on Short Video Platforms. Communications in Humanities Research,67,44-49.
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References

[1]. Dai, P. (2023). The Evolution of Chinese Internet Culture: A Study on the Social Media Platforms’ Role and Their Impact on Online Trends. Communications in Humanities Research, 12, 254-262.

[2]. Anikina, A. (2020). Algorithmic superstructuring: Aesthetic regime of algorithmic governance. Transformations: Journal of Media, Culture and Technology, 34, 35-48.

[3]. Praditya, N. W. P. Y., Permanasari, A. E., & Hidayah, I. (2021). Literature review recommendation system using hybrid method (collaborative filtering & content-based filtering) by utilizing social media as marketing. Computer Engineering and Applications Journal, 10(2), 105-113.

[4]. Gaafar, A. S., Dahr, J. M., & Hamoud, A. K. (2022). Comparative analysis of performance of deep learning classification approach based on LSTM-RNN for textual and image datasets. Informatica, 46(5).

[5]. Chen, Y., & Huang, J. (2024). Effective content recommendation in new media: Leveraging algorithmic approaches. IEEE Access.

[6]. Xing, G. (2023). Study on the Marketing Strategy of ByteDance Company in the Internet Industry-Taking TikTok as an Example. Siam University.

[7]. Wu, X. (2021). A qualitative analysis on Xiaohongshu: Conspicuous consumption, gender, social media algorithms and surveillance.

[8]. Balogun, S. K., & Aruoture, E. (2024). Cultural homogenization vs. cultural diversity: Social media's double-edged sword in the age of globalization. African Journal of Social and Behavioural Sciences, 14(4).

[9]. Huang, X. (2022, April 22). Repackaged, but Douyin and Kuaishou can't quit "content garbage." Woshipm. https://www.woshipm.com/operate/5407015.html

[10]. Jaffe, E. M. (2022). Algorithms, Filters, and Anonymous Messaging: The Addictive Dark Side of Social Media. J. High Tech. L., 23, 260.

Cite this article

Qin,Y. (2025). Analysis of the Homogeneity of Algorithm Recommendation-driven Content Creation on Short Video Platforms. Communications in Humanities Research,67,44-49.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of ICLLCD 2025 Symposium: Enhancing Organizational Efficiency and Efficacy through Psychology and AI

ISBN：978-1-80590-115-0(Print) / 978-1-80590-116-7(Online)
Editor：Rick Arrowood
Conference website: https://2025.icllcd.org/Boston.html
Conference date: 12 May 2025
Series: Communications in Humanities Research
Volume number: Vol.67
ISSN：2753-7064(Print) / 2753-7072(Online)

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References

[1]. Dai, P. (2023). The Evolution of Chinese Internet Culture: A Study on the Social Media Platforms’ Role and Their Impact on Online Trends. Communications in Humanities Research, 12, 254-262.

[2]. Anikina, A. (2020). Algorithmic superstructuring: Aesthetic regime of algorithmic governance. Transformations: Journal of Media, Culture and Technology, 34, 35-48.

[3]. Praditya, N. W. P. Y., Permanasari, A. E., & Hidayah, I. (2021). Literature review recommendation system using hybrid method (collaborative filtering & content-based filtering) by utilizing social media as marketing. Computer Engineering and Applications Journal, 10(2), 105-113.

[4]. Gaafar, A. S., Dahr, J. M., & Hamoud, A. K. (2022). Comparative analysis of performance of deep learning classification approach based on LSTM-RNN for textual and image datasets. Informatica, 46(5).

[5]. Chen, Y., & Huang, J. (2024). Effective content recommendation in new media: Leveraging algorithmic approaches. IEEE Access.

[6]. Xing, G. (2023). Study on the Marketing Strategy of ByteDance Company in the Internet Industry-Taking TikTok as an Example. Siam University.

[7]. Wu, X. (2021). A qualitative analysis on Xiaohongshu: Conspicuous consumption, gender, social media algorithms and surveillance.

[8]. Balogun, S. K., & Aruoture, E. (2024). Cultural homogenization vs. cultural diversity: Social media's double-edged sword in the age of globalization. African Journal of Social and Behavioural Sciences, 14(4).

[9]. Huang, X. (2022, April 22). Repackaged, but Douyin and Kuaishou can't quit "content garbage." Woshipm. https://www.woshipm.com/operate/5407015.html

[10]. Jaffe, E. M. (2022). Algorithms, Filters, and Anonymous Messaging: The Addictive Dark Side of Social Media. J. High Tech. L., 23, 260.

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