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.

该研究首次研究推文情绪动态与心理健康障碍之间的关系，发现言语情绪动态指标与自我公开的诊断之间存在显著差异，这为语言线索在心理疾病的诊断和管理中发挥关键作用提供了重要的初步证据。

语言和心理健康：从文本中衡量情绪动态作为语言生物社会标志物

Language and Mental Health: Measures of Emotion Dynamics from Text as  Linguistic Biosocial Markers

Emerging psychopathology studies are showing that patterns of changes in
emotional state -- emotion dynamics -- are associated with overall well-being
and mental health. More recently, there has been some work in tracking emotion
dynamics through one's utterances, allowing for data to be collected on a
larger scale across time and people. However, several questions about how
emotion dynamics change with age, especially in children, and when determined
through children's writing, remain unanswered. In this work, we use both a
lexicon and a machine learning based approach to quantify characteristics of
emotion dynamics determined from poems written by children of various ages. We
show that both approaches point to similar trends: consistent increasing
intensities for some emotions (e.g., anger, fear, joy, sadness, arousal, and
dominance) with age and a consistent decreasing valence with age. We also find
increasing emotional variability, rise rates (i.e., emotional reactivity), and
recovery rates (i.e., emotional regulation) with age. These results act as a
useful baselines for further research in how patterns of emotions expressed by
children change with age, and their association with mental health.

本研究使用词汇表和基于机器学习的方法来量化儿童诗歌中表现出的情绪动态特征，结果表明随着年龄的增长，某些情绪（例如愤怒、恐惧、快乐、悲伤、唤醒和支配）的强度持续增加，价值持续下降，并且情绪变异性、上升速率（即情绪反应性）和恢复速率（即情绪调节）与年龄增长而不断增加。这些结果为进一步研究儿童表达的情绪模式如何随着年龄变化以及它们与心理健康的关联提供了有用的基线。