  Social network plays an important role in propagating people's viewpoints,
emotions, thoughts, and fears. Notably, following lockdown periods during the
COVID-19 pandemic, the issue of depression has garnered increasing attention,
with a significant portion of individuals resorting to social networks as an
outlet for expressing emotions. Using deep learning techniques to discern
potential signs of depression from social network messages facilitates the
early identification of mental health conditions. Current efforts in detecting
depression through social networks typically rely solely on analyzing the
textual content, overlooking other potential information. In this work, we
conduct a thorough investigation that unveils a strong correlation between
depression and negative emotional states. The integration of such associations
as external knowledge can provide valuable insights for detecting depression.
Accordingly, we propose a multi-task training framework, DeSK, which utilizes
shared sentiment knowledge to enhance the efficacy of depression detection.
Experiments conducted on both Chinese and English datasets demonstrate the
cross-lingual effectiveness of DeSK.
