The Effects of Douyin's Algorithmic Filter Bubble: An Empirical Investigation of Emotional, Social, and National Value Implications for Chinese University Students

Personalized recommendations have become the dominant mode of information dissemination on social media. While algorithmic recommendations offer users information filtering and convenience, they can also lead to prolonged immersion in a single, homogenous content stream, creating information silos and the resulting "filter bubble" effect, which has garnered widespread academic attention. The extent to which filter bubbles influence users’ emotions, social interactions, and values has been a topic of recent research. Drawing on the perspective of social cognitive theory, this study empirically examines the impact of filter bubbles on Chinese university students' social interactions (behavior), emotions (individual), and social consciousness (environment). Through questionnaire surveys and data analysis, the study found that the more homogenous the content of algorithmic recommendations, the more significant the influence on students' social interactions, emotions, and social consciousness. Long-term recommendations of homogenous content can negatively impact user emotions. Furthermore, the study highlights the important role of media literacy education in mitigating filter bubbles and suggests that universities should prioritize media literacy education for students to mitigate the negative impacts of the prevalence of algorithmic recommendations.