An Analysis of the Impacts of Short-Video Recommendation Algorithms on Students' Content Viewing Preferences

Authors

  • Runze Xu School of Creative Media, City University of Hong Kong, Shenzhen, 518001, China

DOI:

https://doi.org/10.54097/6fxea045

Keywords:

Short Videos; Recommendation Algorithm; Student Group; Viewing Preferences; Algorithm Cocoon; Media Literacy.

Abstract

Widely adopted by students for both information access and daily leisure, short video platforms use personalized recommendation systems to steadily shape teenagers’ content choices and viewing habits. This article explores the mechanism by which short video recommendation algorithms affect the content preferences of students at different educational stages. The research adopts the method of literature analysis, integrates the online survey of minors in China Internet Network Information Center, the questionnaire of college students' media behavior, and the relevant results of Tsinghua University's media and information cocoon research, and conducts a comprehensive analysis in combination with communication literature. Research data show that most students passively receive algorithm-recommended content and have insufficient willingness to actively retrieve information. The algorithm relies on user tags to achieve precise distribution, which can not only broaden students' extracurricular knowledge boundaries, but also easily solidify their interest orientation, thereby forming an information cocoon. Students tend to view content that is more entertaining and fragmented, which will continue to weaken their ability to engage in deep viewing and independent thinking. Based on the cognitive development laws of adolescents, this article dialectically analyzes the dual impact of algorithms and proposes improvement ideas from the perspectives of individuals, platforms, campus education, and industry supervision, which can provide practical references for media literacy cultivation, algorithm optimization, and online protection of minors.

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References

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Published

16-07-2026

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Section

Articles

How to Cite

Xu, R. (2026). An Analysis of the Impacts of Short-Video Recommendation Algorithms on Students’ Content Viewing Preferences. International Journal of Education and Social Development, 7(3), 35-39. https://doi.org/10.54097/6fxea045