@inproceedings{varadarajan-etal-2025-linking,
title = "Linking Language-based Distortion Detection to Mental Health Outcomes",
author = "Varadarajan, Vasudha and
Lahnala, Allison and
Vankudari, Sujeeth and
Raghavan, Akshay and
Feltman, Scott and
Mahwish, Syeda and
Ruggero, Camilo and
Kotov, Roman and
Schwartz, H. Andrew",
editor = "Zirikly, Ayah and
Yates, Andrew and
Desmet, Bart and
Ireland, Molly and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ophir, Yaakov",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clpsych-1.5/",
doi = "10.18653/v1/2025.clpsych-1.5",
pages = "62--68",
ISBN = "979-8-89176-226-8",
abstract = "Recent work has suggested detection of cognitive distortions as an impactful task for NLP in the clinical space, but the connection between language-detected distortions and validated mental health outcomes has been elusive. In this work, we evaluate the co-occurrence of (a) 10 distortions derived from language-based detectors trained over two common distortion datasets with (b) 12 mental health outcomes contained within two new language-to-mental-health datasets: DS4UD and iHiTOP. We find higher rates of distortions for those with greater mental health condition severity (ranging from r = 0.16 for thought disorders to r = 0.46 for depressed mood), and that the specific distortions of should statements and fortune telling were associated with a depressed mood and being emotionally drained, respectively. This suggested that language-based assessments of cognitive distortion could play a significant role in detection and monitoring of mental health conditions."
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%0 Conference Proceedings
%T Linking Language-based Distortion Detection to Mental Health Outcomes
%A Varadarajan, Vasudha
%A Lahnala, Allison
%A Vankudari, Sujeeth
%A Raghavan, Akshay
%A Feltman, Scott
%A Mahwish, Syeda
%A Ruggero, Camilo
%A Kotov, Roman
%A Schwartz, H. Andrew
%Y Zirikly, Ayah
%Y Yates, Andrew
%Y Desmet, Bart
%Y Ireland, Molly
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ophir, Yaakov
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-226-8
%F varadarajan-etal-2025-linking
%X Recent work has suggested detection of cognitive distortions as an impactful task for NLP in the clinical space, but the connection between language-detected distortions and validated mental health outcomes has been elusive. In this work, we evaluate the co-occurrence of (a) 10 distortions derived from language-based detectors trained over two common distortion datasets with (b) 12 mental health outcomes contained within two new language-to-mental-health datasets: DS4UD and iHiTOP. We find higher rates of distortions for those with greater mental health condition severity (ranging from r = 0.16 for thought disorders to r = 0.46 for depressed mood), and that the specific distortions of should statements and fortune telling were associated with a depressed mood and being emotionally drained, respectively. This suggested that language-based assessments of cognitive distortion could play a significant role in detection and monitoring of mental health conditions.
%R 10.18653/v1/2025.clpsych-1.5
%U https://aclanthology.org/2025.clpsych-1.5/
%U https://doi.org/10.18653/v1/2025.clpsych-1.5
%P 62-68
Markdown (Informal)
[Linking Language-based Distortion Detection to Mental Health Outcomes](https://aclanthology.org/2025.clpsych-1.5/) (Varadarajan et al., CLPsych 2025)
ACL
- Vasudha Varadarajan, Allison Lahnala, Sujeeth Vankudari, Akshay Raghavan, Scott Feltman, Syeda Mahwish, Camilo Ruggero, Roman Kotov, and H. Andrew Schwartz. 2025. Linking Language-based Distortion Detection to Mental Health Outcomes. In Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025), pages 62–68, Albuquerque, New Mexico. Association for Computational Linguistics.