Multi-Task Learning for Mental Health using Social Media Text
This work addresses mental health monitoring for at-risk individuals using social media, but it is incremental as it applies an existing multi-task learning method to this domain.
The paper tackles the problem of estimating suicide risk and mental health from social media text using a multi-task learning framework, achieving AUC > 0.8 for predicting potential suicide attempts and atypical mental health, with large improvements on tasks with limited data.
We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework. By modeling multiple conditions, the system learns to make predictions about suicide risk and mental health at a low false positive rate. Conditions are modeled as tasks in a multi-task learning (MTL) framework, with gender prediction as an additional auxiliary task. We demonstrate the effectiveness of multi-task learning by comparison to a well-tuned single-task baseline with the same number of parameters. Our best MTL model predicts potential suicide attempt, as well as the presence of atypical mental health, with AUC > 0.8. We also find additional large improvements using multi-task learning on mental health tasks with limited training data.