Feature Disentangling Dual-stream Network for User Bias Alleviation in Social Media Prediction

Abstract

Social media popularity prediction is increasingly crucial for optimizing user engagement and guiding content recommendation systems. However, existing methods suffer from an excessive reliance on user information, which disproportionately influences predictions and leads to the neglect of content diversity. This oversight results in user bias, which adversely impacts the accuracy of predictions. In this paper, an approach named Feature Disentangling Dual-Stream Network (FDDN) is introduced to address this gap. In FDDN, we introduce the Multimodal Extraction Module to extract content features from different modalities. Additionally, the User Popularity Extraction Module helps to analyze the impact of user features on popularity. The Disentangled Adaptation Module distinguishes the impact of user features from content features, thus alleviating user bias and ensuring a more comprehensive prediction. Extensive experiments on a large public dataset demonstrate the robustness and effectiveness of our approach, indicating superior performance compared to existing methods.