Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses

Abstract

Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions. In this work, we investigate these correlations both in terms of literature and as a data analysis problem. We also introduce a novel state-of-the-art transfer learning-based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones. Our model achieves strong performance, with F1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent.