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

TL;DR

This study explores how emotion dynamics derived from Twitter text correlate with mental health diagnoses, suggesting linguistic cues as potential biosocial markers for mental illnesses.

Contribution

It is the first to link tweet emotion dynamics with mental health disorders, demonstrating their potential as diagnostic markers.

Findings

Valence was higher in control users than in those with mental health disorders.

Valence variability was lower in control users compared to several mental health conditions.

Emotion dynamics metrics differed significantly between control and diagnosed groups.

Abstract

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.