Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language

Kate Loveys, Patrick Crutchley, Emily Wyatt, Glen Coppersmith


Abstract
Many psychological phenomena occur in small time windows, measured in minutes or hours. However, most computational linguistic techniques look at data on the order of weeks, months, or years. We explore micropatterns in sequences of messages occurring over a short time window for their prevalence and power for quantifying psychological phenomena, specifically, patterns in affect. We examine affective micropatterns in social media posts from users with anxiety, eating disorders, panic attacks, schizophrenia, suicidality, and matched controls.
Anthology ID:
W17-3110
Volume:
Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality
Month:
August
Year:
2017
Address:
Vancouver, BC
Venues:
CLPsych | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
85–95
Language:
URL:
https://aclanthology.org/W17-3110
DOI:
10.18653/v1/W17-3110
Bibkey:
Cite (ACL):
Kate Loveys, Patrick Crutchley, Emily Wyatt, and Glen Coppersmith. 2017. Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language. In Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology — From Linguistic Signal to Clinical Reality, pages 85–95, Vancouver, BC. Association for Computational Linguistics.
Cite (Informal):
Small but Mighty: Affective Micropatterns for Quantifying Mental Health from Social Media Language (Loveys et al., 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-3110.pdf