From Immersion to Insularity: How Personalized Recommendations Shape User Behavior and Information Cocooning on Douyin

Linfang Li; Balamuralithara Balakrishnan

doi:10.53797/icccmjssh.v5i1.1.2026

Authors

Linfang Li UPSI
Balamuralithara Balakrishnan

DOI:

https://doi.org/10.53797/icccmjssh.v5i1.1.2026

Keywords:

Personalised recommendation systems1; Algorithmic bias2; User behaviour3; Digital media governance4.

Abstract

This paper examines the operational principles and socio-behavioral impacts of personalized recommendation systems on short video platforms, with a particular focus on their role in shaping immersive experiences and contributing to the formation of information cocoons. By leveraging algorithms such as collaborative filtering and deep learning, these systems continuously refine user profiles based on behavioral data, thereby reinforcing content preferences and narrowing information exposure. Drawing on immersion theory and filter bubble theory, this study analyzes how prolonged algorithm-driven engagement leads to unidirectional consumption patterns and diminished cognitive diversity. The article also highlights the risks associated with algorithmic feedback loops, such as attention fixation, preference solidification, and reduced motivation to seek alternative viewpoints. In response, it offers a series of recommendations aimed at users, platforms, and policymakers to promote information diversity, algorithmic transparency, and critical digital literacy. This qualitative analysis contributes to a deeper understanding of algorithmic influence in the contemporary media landscape and proposes pathways toward more balanced and inclusive content ecosystems.

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