Reply to Wang: Clarifying model performance and language markers of depression across races-Reference-Cited by-同舟云学术

Reply to Wang: Clarifying model performance and language markers of depression across races

Published:2024-07-25 Issue:31 Volume:121 Page:
ISSN:0027-8424
Container-title:Proceedings of the National Academy of Sciences
language:en
Short-container-title:Proc. Natl. Acad. Sci. U.S.A.

Author:

Rai Sunny¹²^ORCID,Stade Elizabeth C.³^ORCID,Giorgi Salvatore¹⁴,Francisco Ashley¹^ORCID,Ganesan Adithya V.⁵,Ungar Lyle H.¹^ORCID,Curtis Brenda⁴,Guntuku Sharath C.¹²

Affiliation:

1. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19104

2. Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA 19104

3. Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA 94305

4. Technology & Translational Research Unit, National Institute on Drug Abuse Intramural Research Program, NIH, Baltimore, MD 21224

5. Department of Computer Science, Stony Brook University, Stony Brook, NY 11794

Funder

HHS | NIH | NIDA | Intramural Research Program

HHS | NIH | National Institute on Minority Health and Health Disparities

HHS | NIH | National Institute on Alcohol Abuse and Alcoholism

Publisher

Proceedings of the National Academy of Sciences

Link

https://pnas.org/doi/pdf/10.1073/pnas.2410449121

Reference9 articles.

1. Key language markers of depression on social media depend on race

2. Y. Wang Large language models for depression prediction. Proc. Natl. Acad. Sci. U.S.A. 121 e2409757121 (2024).

3. C. Aguirre K. Harrigian M. Dredze “Gender and racial fairness in depression research using social media” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume P. Merlo J. Tiedemann R. Tsarfaty Eds. (Online: Association for Computational Linguistics 2021) pp. 2932–2949 10.18653/v1/2021.eacl-main.256.

4. R.L. Boyd A. Ashokkumar S. Seraj J.W. Pennebaker The development and psychometric properties of LIWC-22. Austin TX: University of Texas at Austin. (2022).

5. J. Devlin M.W. Chang K. Lee K. Toutanova Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018.