Research in psychopathology has shown that, at an aggregate level, the patterns of emotional change over time -- emotion dynamics -- are indicators of one's mental health. One's patterns of emotion change have traditionally been determined through self-reports of emotions; however, there are known issues with accuracy, bias, and convenience. Recent approaches to determining emotion dynamics from one's everyday utterances, addresses many of these concerns, but it is not yet known whether these measures of utterance emotion dynamics (UED) correlate with mental health diagnoses. Here, for the first time, we study the relationship between tweet emotion dynamics and mental health disorders. We find that each of the UED metrics studied varied by the user's self-disclosed diagnosis. For example: average valence was significantly higher (i.e., more positive text) in the control group compared to users with ADHD, MDD, and PTSD. Valence variability was significantly lower in the control group compared to ADHD, depression, bipolar disorder, MDD, PTSD, and OCD but not PPD. Rise and recovery rates of valence also exhibited significant differences from the control. This work provides important early evidence for how linguistic cues pertaining to emotion dynamics can play a crucial role as biosocial markers for mental illnesses and aid in the understanding, diagnosis, and management of mental health disorders.

翻译：精神病理学研究表明，在总体层面上，情绪随时间变化的模式——情绪动态——是个体心理健康的指标。传统上，个体情绪变化模式通过自我报告的情感来确定；然而，准确性、偏差和便利性方面存在已知问题。近期从日常话语中推断情绪动态的方法解决了其中许多问题，但尚不明确这些话语情绪动态（UED）测量是否与心理健康诊断相关。本文首次研究了推文情绪动态与心理健康障碍之间的关系。我们发现，每项研究的UED指标均因用户自我报告的诊断结果而异。例如：对照组的平均效价（即更积极的文本）显著高于患有ADHD、MDD和PTSD的用户；对照组的效价变异性显著低于患有ADHD、抑郁症、双相障碍、MDD、PTSD和OCD（而非PPD）的用户；效价的上升和恢复速率也与对照组存在显著差异。本研究为情绪动态相关语言线索如何作为精神疾病的生物社会标记发挥关键作用，并助力心理健康障碍的理解、诊断与管理提供了重要的早期证据。