Research Article

From Connection to Isolation: The Role of TikTok Algorithmic Personalization in Computational Media and Cross-cultural Communication

Jue YinShanghai University*

* Corresponding author: [email protected]

Abstract

With the popularity of social media platforms, recommendation algorithms play an important role in user content exposure. However, while the personalized recommendation mechanism of the algorithm may promote cross-cultural communication and understanding, it could also strengthen information barriers and affect users' cultural cognition and cross-cultural communication ability. This paper focuses on TikTok's recommendation algorithm and uses a related studies analysis method to explore its impact on multicultural content exposure and cross-cultural communication. According to the findings of numerous related studies, TikTok's algorithm enhances the probability of users being exposed to specific cultural content through strengthening interest - based matching. Moreover, TikTok's algorithm restricts the extensive dissemination of cross - cultural content on account of its "loneliness effect", thus diminishing the depth and quality of cross - cultural understanding. This paper has revealed the two-sided nature of recommendation algorithms in cross-cultural communication and provides inspiration for improving algorithm design and promoting cross-cultural communication.

Keywords: TikTok; algorithm; personalization; loneliness effect; cross-cultural communication
Published: January 24, 2025
DOI: 10.54254/2753-7064/2025.20620
Volume: CHR Vol.61
pp. 44-52
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References

  1. Ognibene, D., Wilkens, R., Taibi, D., Hernández-Leo, D., Kruschwitz, U., Donabauer, G., ... & Eimler, S. (2023). Challenging social media threats using collective well-being-aware recommendation algorithms and an educational virtual companion. Frontiers in Artificial Intelligence, 5, 654930. https://doi.org/10.3389/frai.2022.654930
  2. Gianola, G., Wyss, D., Bächtiger, A., & Gerber, M. (2024). Empowering local citizens: assessing the inclusiveness of a digital democratic innovation for co-creating a Voting Advice Application. Local Government Studies, 50(1), 174-203. https://doi.org/10.1080/03003930.2023.2185228
  3. Koç, B. (2023). The Role of User Interactions in Social Media on Recommendation Algorithms: Evaluation of TikTok’s Personalization Practices From User’s Perspective [MA thesis]. Istanbul University.
  4. Hu, S., & Zhu, Z. (2022). Effects of social media usage on consumers’ purchase intention in social commerce: a cross-cultural empirical analysis. Frontiers in Psychology, 13, 837752. https://doi.org/10.3389/fpsyg.2022.837752
  5. Scalvini, M. (2023). Making Sense of Responsibility: A Semio-Ethic perspective on TikTok’s algorithmic pluralism. Social Media + Society, 9(2). https://doi.org/10.1177/20563051231180625
  6. Taylor, S. H., & Chen, Y. A. (2024). The lonely algorithm problem: the relationship between algorithmic personalization and social connectedness on TikTok. Journal of Computer-Mediated Communication, 29(5), zmae017. https://doi.org/10.1093/jcmc/zmae017
  7. Boffone, T. (Ed.). (2022). TikTok cultures in the United States. Routledge.
  8. Kaouel, A. (2024). Bridging Cultures or Homogenizing Society? Exploring. Politecnico Di Milano. https://www.politesi.polimi.it/retrieve/cd816259-f6cd-42cc-bd86-9a6dbb6da1a7/2023_05_AliaKaouel.pdf
  9. PHPz. (n.d.). What does TikTok recommended video mean? How to use Douyin to recommend videos? php.cn. https://m.php.cn/faq/724972.html
  10. Collie, N., & Wilson-Barnao, C. (2020). Playing with TikTok: Algorithmic culture and the future of creative work. In The future of creative work (pp. 172-188). Edward Elgar Publishing.
  11. The cultural impacts of TikTok. (2022, December 8). Big Village. https://big-village.com/news/the-cultural-impacts-of-tiktok/
  12. Bhandari, A., & Bimo, S. (2022). Why’s everyone on TikTok now? The algorithmized self and the future of self-making on social media. Social media+ society, 8(1), 20563051221086241.
  13. Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and information technology, 15, 209-227.
  14. Arkhipova, D. (2024). How Artificial Intelligence recommendation systems impact human decision-making. [PhD thesis]. Università di Torino.
  15. Conhyedoss, C. V. (2022, April 28). The cultural awareness that is happening through TikTok and how the platform is being used to create online communities. https://networkconference.netstudies.org/2022/csm/1119/the-cultural-awareness-that-is-happening-through-tiktok-and-how-the-platform-is-being-used-to-create-online-communities/
  16. Shutsko, A. (2020). User-Generated short video content in social media. A case study of TikTok. In Lecture notes in computer science (pp. 108–125). https://doi.org/10.1007/978-3-030-49576-3_8
  17. Nguyen, K. M., Nguyen, N. T., Ngo, N. T. Q., Tran, N. T. H., & Nguyen, H. T. T. (2024). Investigating Consumers’ Purchase Resistance Behavior to AI-Based Content Recommendations on Short-Video Platforms: A Study of Greedy And Biased Recommendations. Journal of Internet Commerce, 23(3), 284-327.
  18. Jawad, M., Talreja, K., Bhutto, S. A., & Faizan, K. (2024). Investigating how AI Personalization Algorithms Influence Self-Perception, Group Identity, and Social Interactions Online. Review of Applied Management and Social Sciences, 7(4), 533-550. https://doi.org/10.47067/ramss.v7i4.397