Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts

Siyuan Chen, Zhiling Zhang, Mengyue Wu, Kenny Q. Zhu

Submission Type: Regular Long Paper

Submission Track: NLP Applications

Keywords: Mental disease detection, symptom, multi-task learning, interpretability, social media

Abstract:

Existing Mental Disease Detection (MDD) research largely studies the detection of a single disorder, overlooking the fact that mental diseases might occur in tandem. Many approaches are not backed by domain knowledge (e.g., psychiatric symptoms) and thus fail to produce interpretable results.
To tackle these issues, we propose an MDD framework that is capable of learning the shared clues of all diseases, while also capturing the specificity of each single disease. The two-stream architecture which simultaneously processes text and symptom features can combine
the strength of both modalities and offer knowledge-based explainability. Experiments on the detection of 7 diseases show that our model can boost detection performance by more than 10\%, especially in relatively rare classes.

Submission Number: 1376