Improving Minority Stress Detection with Emotions
Jonathan Ivey Susan Gauch
Psychological stress detection is an important task for mental healthcare
research, but there has been little prior work investigating the effectiveness
of psychological stress models on minority individuals, who are especially
vulnerable to poor mental health outcomes. In this work, we use the related
task of minority stress detection to evaluate the ability of psychological
stress models to understand the language of sexual and gender minorities. We
find that traditional psychological stress models underperform on minority
stress detection, and we propose using emotion-infused models to reduce that
performance disparity. We further demonstrate that multi-task psychological
stress models outperform the current state-of-the-art for minority stress
detection without directly training on minority stress data. We provide
explanatory analysis showing that minority communities have different
distributions of emotions than the general population and that emotion-infused
models improve the performance of stress models on underrepresented groups
because of their effectiveness in low-data environments, and we propose that
integrating emotions may benefit underrepresented groups in other mental health
detection tasks.