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

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Main finding identifies how visual appeal and posting times drive the virality of artistic content across platforms.
Key evidence shows predictive models achieve 87.3% accuracy in forecasting viral potential of art posts.
The approach combines machine learning and advanced visualization to analyze 500,000 art-related posts over 12 months.
Significance lies in providing actionable insights for artists and cultural institutions on optimizing content and engagement.

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Analysis reveals key dissemination patterns in artistic content on social media, indicating how engagement mechanisms work.

Bonan Li (2025) studied this question.

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