Accessing the Impact of TikTok’s Algorithm on Regional Inequality in Health Information
Jinhui Li, Wen Shi
Abstract
This study aims to audit the potential algorithmic bias in TikTok’s health-related video recommendation toward geographically diverse groups in China. We employed 120 cloud phones and conducted two agent-based testing experiments simulating users’ geographical locations and online behaviors. The results indicated significant regional inequality in video sources recommended by the TikTok algorithm, t(118) = 3.02, p = .003, with users from developed cities encountering a higher proportion of professional videos than those from underdeveloped cities. However, when users from both regions expressed a similar preference for the same type of information, an equal proportion of professional videos was recommended. Our findings suggest that widely used algorithms may covertly perpetuate social inequities and reinforce preexisting class-based inequalities, particularly affecting vulnerable population from low-income regions. This study also highlights the importance of enhancing eHealth literacy among disadvantaged users to mitigate problematic outcomes in the AI-based communication landscape.