Multimodal Data Fusion for Depression Detection on Twitter

Depression is one of the most significant and prevalent mental health disorders in today’s world. Early detection of depression is critical, and this study aims to identify depression in individuals using information derived from social media. The use of social media for various purposes has grown in recent years, as these platforms provide valuable insights into both individuals and society. Social media can be effectively utilized to detect depression. Researchers have attempted to identify depression using various types of data, such as images, text, and audio. Most studies have focused on using only one type of data, such as text or images, for detection. While these methods have achieved notable results, they have limitations in accuracy that can be addressed by incorporating new methods and integrating multiple data modalities into the model. In this study, we propose a multimodal model that analyzes text and images together to detect depression. Compared to similar models, our approach achieves an approximate 5% improvement in accuracy, reaching 89.87%, while utilizing significantly less of the original dataset.