Fairness in Cross-Cultural Digital Content Dissemination: Algorithmic Mediation between Chinese and Western Contexts

This study interrogates how algorithmic systems mediate fairness when Chinese and Western digital contents circulate across cultural borders. Using 5.9 million TikTok, YouTube, Facebook and Bilibili posts (2021-2023), we quantify three tensions: visibility (English #chinatravel clips attain 4.7× raw and 100× adjusted impressions vs. Chinese #中国旅行); values (8.2 % of Chinese political commentary on YouTube is demonetized vs. 1.9 % English equivalents; 11.4 % of Western LGBTQ+ videos are suppressed inside mainland China vs. 2.3 % globally); and voice (cross-border appeals succeed 27 % less often, driven by linguistic and contextual blind spots). Borrowing from Chinese smart-court audit trails, we propose culture-aware procedural logs, layered cultural translations for model explanations, and rotating creator councils with veto power pilots already cut false takedowns by 19 %. Fairness, we argue, is not a universal equation but a negotiated equilibrium between algorithmic logics and culturally embedded notions of justice.