Early identification of depression is beneficial to public health surveillance and disease treatment. There are many models that mainly treat the detection as a binary classification task, such as detecting whether a user is depressed. However, identifying users’ depression severity levels from posts on social media is more clinically useful for future prevention and treatment. Existing severity detection methods mainly model the semantic information of posts while ignoring the relevant sentiment information, which can reflect the user’s state of mind and could be helpful for severity detection. In addition, they treat all severity levels equally, making the model difficult to distinguish between closely-labeled categories. We propose a sentiment-guided Transformer model, which efficiently fuses social media posts’ semantic information with sentiment information. Furthermore, we also utilize a supervised severity-aware contrastive learning framework to enable the model to better distinguish between different severity levels. The experimental results show that our model achieves superior performance on two public datasets, while further analysis proves the effectiveness of all proposed modules.

Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment. Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention. Recent advances in pretrained contextualized language representations have promoted the development of several domainspecific pretrained models and facilitated several downstream applications. However, there are no existing pretrained language models for mental healthcare. This paper trains and release two pretrained masked language models, i.e., MentalBERT and MentalRoBERTa, to benefit machine learning for the mental healthcare research community. Besides, we evaluate our trained domain-specific models and several variants of pretrained language models on several mental disorder detection benchmarks and demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks.