Examining the Cultural Impact of Algorithmic Curation on Artistic Production

Abstract

Algorithmic curation has become a central force in shaping contemporary artistic production, mediating how art is discovered, disseminated, and consumed across digital platforms. This research investigates the cultural effects of algorithmically curated recommendations on creative processes, artistic visibility, and audience reception. Drawing upon theories of cultural production, media sociology, and platform studies, the study examines how algorithmic filtering influences artistic diversity, genre innovation, and cultural hierarchies. A mixed-method approach was employed, combining survey data from 320 professional artists and cultural producers with content analysis of streaming and social media platforms. Structural Equation Modeling using SmartPLS assessed the relationships between algorithmic exposure, visibility, creative autonomy, audience feedback, and cultural impact. Results indicate that algorithmic curation significantly affects the visibility of certain genres and artist profiles, favoring mainstream and algorithmically optimized content. Creative autonomy mediates the effect of algorithmic visibility on perceived cultural influence, with artists adapting production strategies to align with algorithmic preferences. Audience engagement amplifies these effects, reinforcing patterns of cultural reinforcement and selective exposure. The study contributes to understanding the interplay between technological infrastructures and cultural production by demonstrating that algorithmic curation not only redistributes attention but actively shapes the creative choices of artists. Practically, findings suggest that platform governance, transparency, and support for diverse artistic expressions are essential to mitigating homogenization risks. Limitations include self-reported measures and platform-specific sampling. Future research should examine cross-platform dynamics and longitudinal changes in artistic practices under evolving algorithmic architectures.