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

Daniela Teodorescu, Tiffany Cheng, Alona Fyshe, Saif Mohammad


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 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.
Anthology ID:
2023.emnlp-main.188
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3117–3133
Language:
URL:
https://aclanthology.org/2023.emnlp-main.188
DOI:
10.18653/v1/2023.emnlp-main.188
Bibkey:
Cite (ACL):
Daniela Teodorescu, Tiffany Cheng, Alona Fyshe, and Saif Mohammad. 2023. Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3117–3133, Singapore. Association for Computational Linguistics.
Cite (Informal):
Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers (Teodorescu et al., EMNLP 2023)
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