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Abstract

The rapid growth of social media platforms has radically changed the dynamics by which artistic content is disseminated, enabling the development of new paradigms for artistic production and audience engagement. This study undertakes an in-depth examination and visualization framework aimed at explicating the spread of artistic content on major social media platforms. By consolidating and analyzing data from Instagram, Twitter, TikTok, Pinterest, and Behance, this study examines 500,000 art-related posts over a 12-month period to identify key dissemination patterns and viral dynamics. The methodological framework utilizes state-of-the-art machine learning algorithms, including deep neural networks for extracting visual features and graph-based approaches for modeling diffusion dynamics, supplemented by advanced visualization techniques to explain complex dynamics of dissemination. Findings reveal that the spread of artistic content follows certain temporal and spatial dynamics, with the visual appeal of an artwork, posting times, and network effects constituting key drivers of virality. The visualization framework utilized integrates interactive network graphs, temporal heat maps, and multi-dimensional scaling to represent dissemination pathways, thus enabling real-time tracking and pattern detection. The predictive models achieve an accuracy level of 87.3% in predicting the viral potential, reflecting a significant performance boost compared to conventional baseline techniques. This study offers novel insights into digital art consumption, provides actionable suggestions for artists and cultural institutions, and establishes a theoretical foundation for understanding the diffusion of creative content in interconnected systems. The proposed framework has practical implications in terms of how content can be optimized, audience engagement enhanced, and platform design improved, effectively bridging the gap between computational social science and problems relevant to digital humanities.

Keywords

social media analytics art content dissemination network visualization viral propagation patterns digital art consumption machine learning information diffusion cultural analytics

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Analysis and visualization research of art content dissemination patterns on social media

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