  Mental disease detection (MDD) from social media has suffered from poor
generalizability and interpretability, due to lack of symptom modeling. This
paper introduces PsySym, the first annotated symptom identification corpus of
multiple psychiatric disorders, to facilitate further research progress. PsySym
is annotated according to a knowledge graph of the 38 symptom classes related
to 7 mental diseases complied from established clinical manuals and scales, and
a novel annotation framework for diversity and quality. Experiments show that
symptom-assisted MDD enabled by PsySym can outperform strong pure-text
baselines. We also exhibit the convincing MDD explanations provided by symptom
predictions with case studies, and point to their further potential
applications.
