MOGAM: A Multimodal Object-oriented Graph Attention Model for Depression
  Detection
Junyeop Cha Seoyun Kim Dongjae Kim Eunil Park
Early detection plays a crucial role in the treatment of depression.
Therefore, numerous studies have focused on social media platforms, where
individuals express their emotions, aiming to achieve early detection of
depression. However, the majority of existing approaches often rely on specific
features, leading to limited scalability across different types of social media
datasets, such as text, images, or videos. To overcome this limitation, we
introduce a Multimodal Object-Oriented Graph Attention Model (MOGAM), which can
be applied to diverse types of data, offering a more scalable and versatile
solution. Furthermore, to ensure that our model can capture authentic symptoms
of depression, we only include vlogs from users with a clinical diagnosis. To
leverage the diverse features of vlogs, we adopt a multimodal approach and
collect additional metadata such as the title, description, and duration of the
vlogs. To effectively aggregate these multimodal features, we employed a
cross-attention mechanism. MOGAM achieved an accuracy of 0.871 and an F1-score
of 0.888. Moreover, to validate the scalability of MOGAM, we evaluated its
performance with a benchmark dataset and achieved comparable results with prior
studies (0.61 F1-score). In conclusion, we believe that the proposed model,
MOGAM, is an effective solution for detecting depression in social media,
offering potential benefits in the early detection and treatment of this mental
health condition.