Enhancing Stress Detection on Social Media Through Multi-Modal Fusion of Text and Synthesized Visuals

Social media platforms generate an enormous volume of multi-modal data, yet stress detection research has predominantly relied on textbased analysis.In this work, we propose a novel framework that integrates textual content with synthesized visual cues to enhance stress detection.Using the generative model DALLE, we synthesize images from social media posts, which are then fused with text through the multi-modal capabilities of a pre-trained CLIP model, which encodes both text and image data into a shared semantic space.Our approach is evaluated on the Dreaddit dataset, where a classifier trained on frozen CLIP features achieves 94.90% accuracy, and full fine-tuning further improves performance to 98.41%.These results underscore the integration of synthesized visuals with textual data not only enhances stress detection but also offers a robust method over traditional text-only methods, paving the way for innovative approaches in mental health monitoring and social media analytics.