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.

The population of the earth is constantly growing and therefore also the demand for food. In consequence, breeding crop plants which most efficiently make use of the available cropland is one of the greatest challenges nowadays. In particular, plants which are resilient and resistant to environmental stresses are desirable. The development of such plants relies on the investigation of the interaction between the plant's genes and the environmental stresses. In order to be able to investigate the interaction a quantitative representation of the environmental stresses is needed. Here, we consider this representation combining state-of-the-art data-driven methods with expert-driven modeling from agriculture. Briefly put, it has been reported that environmental stress such as inappropriate or extreme temperature conditions, lack of sufficient moisture, etc., can significantly impede the life cycle development of corn, thus leading to yield reductions (cf.