Selective information exposure under algorithms: How filter bubbleof DouYinaffect college students' emotions attitudes and ideologies

Personalized algorithms have emerged as a predominant method of information dissemination on social media platforms. While these algorithms offer users the convenience of tailored information and filtering capabilities, they concurrently risk obscuring diverse viewpoints and cultural values. This can result in users being perpetually exposed to a homogenized information milieu, engendering an "information cocoon" or what is colloquially termed as a "filter bubble." Given its status as the world's most rapidly expanding social platform, TikTok's filter bubbles have garnered significant attention from Chinese national governance, especially considering their potential implications for the social interactions, emotional well-being, and ideological leanings of its primary demographic: Chinese university students. Through rigorous data analysis and empirical investigation, this study discerns that key determinants within these filter bubbles—namely, information homogenization, customization, and singularity of information sources—profoundly impact the social behaviors, emotions, and ideologies of university attendees. Notably, heightened levels of information homogenization within algorithmically curated filter bubbles correlate with pronounced social segmentation among students. Conversely, when emotional expression becomes more straightforward, there tends to be a diminished ideological influence.