Our study presents a framework for predicting image-based social media content popularity that focuses on addressing complex image information and a hierarchical data structure. We utilize the Google Cloud Vision API to effectively extract key image and color information from users' postings, achieving 6.8% higher accuracy compared to using non-image covariates alone. For prediction, we explore a wide range of prediction models, including Linear Mixed Model, Support Vector Regression, Multi-layer Perceptron, Random Forest, and XGBoost, with linear regression as the benchmark. Our comparative study demonstrates that models that are capable of capturing the underlying nonlinear interactions between covariates outperform other methods.

翻译：我们的研究提出了一种预测基于图像的社交媒体内容流行度的框架，该框架专注于处理复杂图像信息和分层数据结构。我们利用Google Cloud Vision API有效提取用户发帖中的关键图像和颜色信息，与仅使用非图像协变量相比，准确率提升了6.8%。在预测方面，我们探索了多种预测模型，包括线性混合模型、支持向量回归、多层感知机、随机森林和XGBoost，并以线性回归作为基准。我们的比较研究表明，能够捕获协变量间潜在非线性交互作用的模型优于其他方法。