Algorithmic Silence as Cultural Governance: Platform Visibility, AI-Generated Chinese Digital Content, and the Unfinished Politics of Cultural Legitimacy
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Beijing Normal-Hong Kong Baptist University
Abstract
This article develops algorithmic silence as a concept for studying how platform visibility governs the circulation and legitimacy of AI-generated Chinese digital content. Whereas existing debates often treat visibility as distribution, popularity, or exposure, the article argues that visibility has become a managed condition through which cultural works become discoverable, rankable, measurable, interpretable, and institutionally recognizable. Drawing on 30 anonymized semi-structured interviews, 178 coded interview excerpts, 240 platform observation records, a 20-code qualitative codebook, and reflexive audit materials, the article examines how recommendation uncertainty, metric hierarchies, template aesthetics, AI labeling, institutional risk management, and unstable platform traces produce forms of silence that do not require explicit deletion or prohibition. The analysis identifies five linked dimensions of algorithmic silence: infrastructural, metric, aesthetic, institutional, and methodological silence. It further proposes slow visibility as a counter-concept for cultural expression whose value depends on time, context, community interpretation, or non-metric recognition. The article contributes to platform studies by reframing visibility as cultural governance, to AIGC studies by foregrounding human-AI co-production and legitimacy struggles, and to methodological debates by showing why partial and unstable traces must be handled through causal restraint. The study does not claim direct access to platform algorithms; instead, it offers an evidence-bounded account of how managed visibility reorganizes cultural recognition.
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The anonymized empirical materials analyzed in this article consist of semi-structured interview excerpts, coding records, and aggregated platform observation materials collected by the author. Due to participant confidentiality and the sensitivity of platform-trace contexts, the full dataset is not publicly available. Selected anonymized quotations, aggregated coding logic, and summary evidence are reported in the manuscript. Limited de-identified supplementary information may be made available by the author upon reasonable request, subject to ethical and confidentiality restrictions.
This work is licensed under a Creative Commons Attribution 4.0 International License.