  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 ease of data collection. 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.
