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.

利用图像信息和层次化数据结构，预测基于图像的社交媒体内容的流行度，通过 Google Cloud Vision API 提取关键图像和颜色信息，相比于单独使用非图像协变量，精度提升了 6.8%。通过线性混合模型、支持向量回归、多层感知机、随机森林和 XGBoost 等多种预测模型，对比实验表明能够捕捉协变量之间的非线性相互作用的模型优于其他方法。