Detecting student depression on Weibo based on various multimodal fusion methods

This study uses a multimodal fusion model for early student depression detection by analysing student data from Sina Weibo. It compares early and late fusion methods with traditional Natural Language Processing models and achieves a 3% accuracy improvement over 100 cycles. The study shows that standardising only structured data without neural network mapping reduces predictive performance. It was also found that while both fusion methods exhibited similar predictive capabilities, the late fusion model exhibited overfitting, suggesting that there is potential for the late fusion strategy to further improve model performance performance. This study summarises the ability of multimodal fusion models to effectively detect early signs of student depression and lays the foundation for future research on model interpretability for early student depression detection and future research on student behaviour analysis.