Algorithmic Internalization and the Paradox of Authenticity: How Platform Governance Shapes Influencer Identity and Labor

This study analyzes the contributions of audience attention and algorithms to the reformulation of digital creators’ creativity, authenticity, and identity. Focused on influencers, this qualitative study seeks to discover how creative individuals cope with and suffer from algorithmic supervision through self-regulation and performance. The study shows that the algorithmic requirements lead creators to pursue authenticity and build rapport with audiences algorithmically. This in turn creates a kind of paradoxical closeness between the creator and audience. The research contributes to the synthesis and integration of curated authenticity, algorithmic pre-structuring, and metricized self-discipline into a single coherent model. The study demonstrates how these factors operate in concert, creating a feedback loop that the study terms the Algorithmic Internalization Framework. It wraps the sociological (external) and psychological (internal) aspects of influencer work.