Enhanced CatBoost with Stacking Features for Social Media Prediction

Abstract

The Social Media Prediction (SMP) challenge aims to predict the future popularity of online posts by leveraging social media data. Social media data contains multimodal information, such as text, images, time series, etc. Previous methods have proposed many feature extraction and feature construction methods to represent these multimodal information, thereby predicting the popularity of posts. Despite the success of previous methods in extracting features from social media data, these features tend to be predominantly lower-order, posing a challenge in accurately capturing the rich information contained in text and images. In this paper, we propose a more diverse feature mining method and introduce a stacking block module to capture higher-order feature information contained in text and images. "lower-order" refers to the original high-dimensional embedding representation, while "high-order" pertains to the impact on post social popularity captured by tree models from text or image. We conducted massive experiments to evaluate the effectiveness of our proposed method and found that the stacking block module significantly improved performance.