We are united in how emotions are central to shaping our experiences; and yet, individuals differ greatly in how we each identify, categorize, and express emotions. In psychology, variation in the ability of individuals to differentiate between emotion concepts is called emotion granularity (determined through self-reports of one's emotions). High emotion granularity has been linked with better mental and physical health; whereas low emotion granularity has been linked with maladaptive emotion regulation strategies and poor health outcomes. In this work, we propose computational measures of emotion granularity derived from temporally-ordered speaker utterances in social media (in lieu of self-reports that suffer from various biases). We then investigate the effectiveness of such text-derived measures of emotion granularity in functioning as markers of various mental health conditions (MHCs). We establish baseline measures of emotion granularity derived from textual utterances, and show that, at an aggregate level, emotion granularities are significantly lower for people self-reporting as having an MHC than for the control population. This paves the way towards a better understanding of the MHCs, and specifically the role emotions play in our well-being.

翻译：情感在塑造我们体验中的核心地位是共通的；然而，个体在识别、分类和表达情感的方式上却存在巨大差异。在心理学中，个体区分不同情感概念能力的差异被称为情感粒度（通常通过个体对自身情感的自陈报告来确定）。高情感粒度与更好的心理和身体健康相关联；而低情感粒度则与适应不良的情绪调节策略及较差的健康结果相关。在本研究中，我们提出了从社交媒体中按时间顺序排列的说话者话语（用以替代存在多种偏差的自陈报告）推导出的情感粒度计算度量方法。随后，我们探究了此类从文本衍生的情感粒度度量作为各种心理健康状况（MHCs）标记的有效性。我们建立了基于文本话语的情感粒度基线度量，并表明在聚合层面上，自我报告患有MHC的人群的情感粒度显著低于对照组人群。这为更好地理解MHCs，特别是情感在我们福祉中所扮演的角色，铺平了道路。