Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk

Vasudha Varadarajan, Allison Lahnala, Adithya V Ganesan, Gourab Dey, Siddharth Mangalik, Ana-Maria Bucur, Nikita Soni, Rajath Rao, Kevin Lanning, Isabella Vallejo, Lucie Flek, H. Andrew Schwartz, Charles Welch, Ryan Boyd


Abstract
Research on psychological risk factors for suicide has developed for decades. However, combining explainable theory with modern data-driven language model approaches is non-trivial. In this study, we propose and evaluate methods for identifying language patterns aligned with theories of suicide risk by combining theory-driven suicidal archetypes with language model-based and relative entropy-based approaches. Archetypes are based on prototypical statements that evince risk of suicidality while relative entropy considers the ratio of how unusual both a risk-familiar and unfamiliar model find the statements. While both approaches independently performed similarly, we find that combining the two significantly improved the performance in the shared task evaluations, yielding our combined system submission with a BERTScore Recall of 0.906. Consistent with the literature, we find that titles are highly informative as suicide risk evidence, despite the brevity. We conclude that a combination of theory- and data-driven methods are needed in the mental health space and can outperform more modern prompt-based methods.
Anthology ID:
2024.clpsych-1.28
Original:
2024.clpsych-1.28v1
Version 2:
2024.clpsych-1.28v2
Volume:
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
Month:
March
Year:
2024
Address:
St. Julians, Malta
Editors:
Andrew Yates, Bart Desmet, Emily Prud’hommeaux, Ayah Zirikly, Steven Bedrick, Sean MacAvaney, Kfir Bar, Molly Ireland, Yaakov Ophir
Venues:
CLPsych | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
278–291
Language:
URL:
https://aclanthology.org/2024.clpsych-1.28
DOI:
Bibkey:
Cite (ACL):
Vasudha Varadarajan, Allison Lahnala, Adithya V Ganesan, Gourab Dey, Siddharth Mangalik, Ana-Maria Bucur, Nikita Soni, Rajath Rao, Kevin Lanning, Isabella Vallejo, Lucie Flek, H. Andrew Schwartz, Charles Welch, and Ryan Boyd. 2024. Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 278–291, St. Julians, Malta. Association for Computational Linguistics.
Cite (Informal):
Archetypes and Entropy: Theory-Driven Extraction of Evidence for Suicide Risk (Varadarajan et al., CLPsych-WS 2024)
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PDF:
https://aclanthology.org/2024.clpsych-1.28.pdf
Video:
 https://aclanthology.org/2024.clpsych-1.28.mp4