Communications in Humanities Research ISSN: 2753-7072
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Research Article

Mechanisms of Platform Algorithm-Driven Content Diffusion in New Media: Evidence from Traffic Allocation Systems

Han LuoNanchang University*

* Corresponding author: [email protected]

Abstract

In the era of platformized communication, algorithm-driven recommendation systems have fundamentally reshaped content diffusion on new media platforms. Unlike traditional communication models shaped by editorial gatekeeping and follower-based networks, contemporary platforms such as TikTok and YouTube Shorts rely on traffic-driven algorithms to accelerate content diffusion and maximize user engagement. This study investigates how platform algorithms reshape content diffusion through traffic allocation mechanisms and explores the communication logic underlying algorithmic amplification. Drawing on recent studies in platformization, algorithmic governance, and information diffusion, this study adopts a qualitative research design combining literature review, comparative platform case analysis, and mechanism analysis. The study focuses on three research questions: how algorithmic recommendations accelerate content dissemination, how traffic mechanisms influence visibility and user interaction, and what communicative outcomes emerge from algorithm-driven diffusion. The findings indicate that personalized recommendations, real-time feedback systems, and algorithmic traffic allocation mechanisms significantly enhance content dissemination efficiency while simultaneously reinforcing the concentration of user attention and emotional amplification. The paper argues that platform algorithms have evolved from technical tools into key actors in the communication process, reshaping both the logic of content dissemination and the structure of digital public communication.

Keywords: Platform Algorithms; Content Diffusion; Traffic Allocation Mechanisms; Algorithmic Recommendation; New Media Communication
Published: August 18, 2026
DOI: 10.54254/2753-7064/2026.HT36235
Volume: CHR Vol.118
pp. 41-48
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References

  1. Nielsen, R. K., & Fletcher, R. (2023). Comparing the platformization of news media systems: A cross-country analysis.European Journal of Communication, 38(5), 484-499.
  2. Chen, C., Wang, Z., & Gn, J. (2023). Serve the traffic: The logic of traffic and its politics on WeChat platform.Social Media+ Society, 9(3), 20563051231193025.
  3. Lasser, J., & Poechhacker, N. (2025). Designing social media content recommendation algorithms for societal good.Annals of the New York Academy of Sciences, 1548(1), 20-28.
  4. Iamnitchi, A., Hall, L. O., Horawalavithana, S., Mubang, F., Ng, K. W., & Skvoretz, J. (2023). Modeling information diffusion in social media: Data-driven observations.Frontiers in Big Data, 6(1).
  5. Meng, Z. (2026). Cross-platform sensitivity and algorithmic adaptability: How transnational creators navigate algorithms across Chinese and US-based platforms.New Media & Society, 28(3), 1171–1189.
  6. Entrena-Serrano, C. (2025). Watch, scroll, repeat: How interface design shapes consumptive curation affordances on TikTok.Social Media+ Society, 11(3), 20563051251358529.
  7. Gausen, A., Guo, C., & Luk, W. (2025). An approach to sociotechnical transparency of social media algorithms using agent-based modelling.AI and Ethics, 5(2), 1827–1845.
  8. Galeazzi, A., Paudel, P., Conti, M., De Cristofaro, E., & Stringhini, G. (2024). Revealing the secret power: How algorithms can influence content visibility on social media.arXiv e-prints, arXiv-2410.
  9. Murdock, I., Carley, K. M., & Yağan, O. (2024). An agent-based model of cross-platform information diffusion and moderation.Social Network Analysis and Mining, 14(1), 145.
  10. Salles, J. (2025). Affect and prediction in short-video social media recommendation algorithm: TikTok and the missing half-second.New Media & Society, 14614448251385086.
  11. Baumann, F., Arora, N., Rahwan, I., & Czaplicka, A. (2026). Dynamics of algorithmic content amplification on TikTok.EPJ Data Science, 15(1), 18.
  12. Coppolillo, E., Mungari, S., Ritacco, E., Fabbri, F., Minici, M., Bonchi, F., & Manco, G. (2025). Algorithmic drift: A simulation framework to study the effects of recommender systems on user preferences.Information Processing & Management, 62(4), 104125.
  13. Chen, Y., Li, F., & Preuss, M. (2025). Algorithmic attention and content creation on social media platforms. Available at SSRN 5182754.
  14. Rathje, S., Robertson, C., Brady, W. J., & Van Bavel, J. J. (2024). People think that social media platforms do (but should not) amplify divisive content.Perspectives on Psychological Science, 19(5), 781-795.
  15. Kim, T., & Im, I. (2025). Understanding users' AI manipulation intention: An empirical investigation of the antecedents in the context of AI recommendation algorithms.Information & Management, 62(1), 104061.
  16. Shin, D., & Shin, E. Y. (2025). Cascading falsehoods: Mapping the diffusion of misinformation in algorithmic environments.AI & Society, 41(2), 1435-1452.
  17. Corsi, G. (2024). Evaluating Twitter's algorithmic amplification of low-credibility content: An observational study.EPJ Data Science, 13(1), 18.
  18. Moon, J. H., Kim, S., Jung, Y., Bang, J., & Sung, Y. (2025). The effects of explainability and user control on algorithmic transparency: The moderating role of algorithmic literacy.Cyberpsychology, Behavior, and Social Networking, 28(7), 497-504.