利用情绪改进少数群体压力检测
Improving Minority Stress Detection with Emotions
AI总结：
本文发现传统心理压力模型在性与性别少数群体上表现不佳，提出用情绪融合模型和多任务学习提升少数群体压力检测性能，并证明其低数据环境优势。
摘要：
心理压力检测是心理健康护理研究中的一项重要任务，但此前很少有工作考察心理压力模型在少数群体个体上的有效性，而这些人尤其容易出现不良心理健康结果。在本工作中，我们使用相关的少数群体压力检测任务来评估心理压力模型理解性与性别少数群体语言的能力。我们发现传统心理压力模型在少数群体压力检测上表现不佳，并提出使用融入情绪信息的模型来缩小这一性能差距。我们进一步证明，多任务心理压力模型在不直接在少数群体压力数据上训练的情况下，超越了当前少数群体压力检测的最先进水平。我们提供的解释性分析表明，少数群体社区的情绪分布与一般人群不同，并且融入情绪信息的模型由于在低数据环境中的有效性而改善了压力模型在代表性不足群体上的表现。我们提出，整合情绪信息可能使其他心理健康检测任务中的代表性不足群体受益。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
