Algorithmic muteness: How algorithmic management obstructs platform content operators’ resonance with their labor

Workers who plan, curate, and promote content fuel platforms’ traffic success, yet frequently experience their labor as leaving no meaningful trace. While critical scholarship links this estrangement to algorithmic management, how it reshapes the quality of workers’ relationship with their own labor remains underexplored. Drawing on Rosa’s Resonance Theory and 27 interviews with platform content operators at China’s leading internet companies, this study reveals how algorithmic management obstructs resonance across content production, distribution, and feedback. Quantification hollows out labor’s normative worth; black-boxing forecloses self-efficacy through the (un)controllability paradox; amplification and acceleration suspend the possibility of self-transformation, even amid measurable success. We conceptualize ‘algorithmic muteness’, which captures a relational state—workers maintain high-intensity, data-mediated contact with their labor, yet this contact rarely yields genuine responsiveness. Algorithmic muteness moves beyond documenting the static, structural consequences of algorithmic management (e.g. alienation, meaninglessness) to tracing the relational process producing them, while critically revealing algorithms’ deep entanglement with platform capital.