Structural Biases in Online Evaluation and Creative Survival: Audience Layers, Cognitive Distortion, and Genre-Specific Saturation Thresholds

This work develops an integrated theoretical model explaining why creators systematically misinterpret online evaluation signals. Drawing on research in cognitive psychology, data bias, cultural evolution, and audience segmentation, the paper demonstrates that negative feedback is structurally overrepresented due to self-selection, extremity bias, and algorithmic amplification. It introduces a three-layer audience model (core, adjacent, external), clarifies the statistical properties of different evaluative signals, and identifies genre-specific saturation thresholds that redefine what constitutes sufficient support in diverse creative domains. The analysis shows that creators often underestimate genuine support and overestimate criticism because visible metrics distort the underlying audience distribution. The paper argues that sustainable creative practice requires recognising the asymmetry between low-cost negative reactions and high-cost acts of endorsement, and reframing criticism as part of a broader developmental trajectory. An appendix further highlights the role of supportive audiences in positive cultural diffusion, emphasising that meaningful advocacy carries greater effort and value than casual disapproval. Together, these insights provide a structural foundation for healthier self-evaluation and long-term creative growth.