Gradient Boost Tree Network based on Extensive Feature Analysis for Popularity Prediction of Social Posts

Social media popularity (SMP) prediction is a complex task, affected by various features such as text, images, and spatial-temporal information. One major challenge in SMP is integrating features from multiple modalities without overemphasizing user-specific details while efficiently capturing relevant user information. This study introduces a robust multi-modality feature mining framework for predicting SMP scores by incorporating additional identity-related features sourced from the official SMP dataset when a user's path alias is accessible. Our preliminary analyses suggest these supplemental features significantly enrich the user-related context, contributing to a substantial improvement in performance and proving that non-identity features are relatively unimportant. This implies that we should focus more on discovering the identity-related features than other meta-data. To further validate our findings, we perform comprehensive experiments investigating the relationship between those identity-related features and scores. Finally, the LightGBM and TabNet are employed within our framework to effectively capture intricate semantic relationships among different modality features and user-specific data. Our experimental results confirm that these identity-related features, especially external ones, significantly improve the prediction performance of SMP tasks.