Built on

G. Gkotsis, A. Oellrich, S. Velupillai, M. Liakata, T. J. Hubbard, R. J. Dobson, and R. Dutta, “Characterisation of mental health conditions in social media using informed deep learning,” Scientific reports , vol. 7, no. 1, p. 45141, 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
P. Inna, “Identifying depression,” https://github.com/Inusette/Identifying-depression , 2018
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
E. Turcan and K. McKeown, “Dreaddit: A reddit dataset for stress analysis in social media,” arXiv preprint arXiv:1911.00133 , 2019
Original
2019
Earlier work this paper cites.
M. Gaur, A. Alambo, J. P. Sain, U. Kursuncu, K. Thirunarayan, R. Kavuluru, A. Sheth, R. Welton, and J. Pathak, “Knowledge-aware assessment of severity of suicide risk for early intervention,” in The world wide web conference , 2019, pp. 514–525
2019
Earlier work this paper cites.

Similar

M. M. Tadesse, H. Lin, B. Xu, and L. Yang, “Detection of depression-related posts in reddit social media forum,” IEEE Access , vol. 7, pp. 44 883–44 893, 2019
2019
Cited alongside, same era.
S. Abuse et al. , “Key substance use and mental health indicators in the united states: results from the 2019 national survey on drug use and health,” 2020
2020
Cited alongside, same era.
S. Chancellor and M. De Choudhury, “Methods in predictive techniques for mental health status on social media: a critical review,” NPJ digital medicine , vol. 3, no. 1, p. 43, 2020
2020
Cited alongside, same era.
J. Kim, J. Lee, E. Park, and J. Han, “A deep learning model for detecting mental illness from user content on social media,” Scientific reports , vol. 10, no. 1, pp. 1–6, 2020
2020
Cited alongside, same era.
G. Chiang and A. Stepanyan, “Insight stress analysis,” https://github.com/gillian850413/Insight_Stress_Analysis , 2020

Then

W. H. Organization et al. , “World mental health report: transforming mental health for all,” 2022
2022
Later among the works it cites.
K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl et al. , “Large language models encode clinical knowledge,” arXiv preprint arXiv:2212.13138 , 2022
Original
2022
Later among the works it cites.
A. Alambo, “Suicide risk assessment using reddit,” https://github.com/AmanuelF/Suicide-Risk-Assessment-using-Reddit , 2022
2022
Later among the works it cites.
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al. , “Sparks of artificial general intelligence: Early experiments with gpt-4,” arXiv preprint arXiv:2303.12712 , 2023
Original
2023
Closest in time.
H. Nori, N. King, S. M. McKinney, D. Carignan, and E. Horvitz, “Capabilities of gpt-4 on medical challenge problems,” arXiv preprint arXiv:2303.13375 , 2023

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2023

Evaluation of ChatGPT for NLP-based Mental Health Applications

Lamichhane, Bishal

Understand

Large language models (LLM) have been successful in several natural language understanding tasks and could be relevant for natural language processing (NLP)-based mental health application research.

In this work, we report the performance of LLM-based ChatGPT (with gpt-3.5-turbo backend) in three text-based mental health classification tasks: stress detection (2-class classification), depression detection (2-class classification), and suicidality detection (5-class classification).
We obtained annotated social media posts for the three classification tasks from public datasets.
Then ChatGPT API classified the social media posts with an input prompt for classification.

Built on

G. Gkotsis, A. Oellrich, S. Velupillai, M. Liakata, T. J. Hubbard, R. J. Dobson, and R. Dutta, “Characterisation of mental health conditions in social media using informed deep learning,” Scientific reports , vol. 7, no. 1, p. 45141, 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
P. Inna, “Identifying depression,” https://github.com/Inusette/Identifying-depression , 2018
2018
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018
Original
2018
Earlier work this paper cites.
E. Turcan and K. McKeown, “Dreaddit: A reddit dataset for stress analysis in social media,” arXiv preprint arXiv:1911.00133 , 2019
Original
2019
Earlier work this paper cites.
M. Gaur, A. Alambo, J. P. Sain, U. Kursuncu, K. Thirunarayan, R. Kavuluru, A. Sheth, R. Welton, and J. Pathak, “Knowledge-aware assessment of severity of suicide risk for early intervention,” in The world wide web conference , 2019, pp. 514–525
2019
Earlier work this paper cites.

Similar

M. M. Tadesse, H. Lin, B. Xu, and L. Yang, “Detection of depression-related posts in reddit social media forum,” IEEE Access , vol. 7, pp. 44 883–44 893, 2019
2019
Cited alongside, same era.
S. Abuse et al. , “Key substance use and mental health indicators in the united states: results from the 2019 national survey on drug use and health,” 2020
2020
Cited alongside, same era.
S. Chancellor and M. De Choudhury, “Methods in predictive techniques for mental health status on social media: a critical review,” NPJ digital medicine , vol. 3, no. 1, p. 43, 2020
2020
Cited alongside, same era.
J. Kim, J. Lee, E. Park, and J. Han, “A deep learning model for detecting mental illness from user content on social media,” Scientific reports , vol. 10, no. 1, pp. 1–6, 2020
2020
Cited alongside, same era.
G. Chiang and A. Stepanyan, “Insight stress analysis,” https://github.com/gillian850413/Insight_Stress_Analysis , 2020

Then

W. H. Organization et al. , “World mental health report: transforming mental health for all,” 2022
2022
Later among the works it cites.
K. Singhal, S. Azizi, T. Tu, S. S. Mahdavi, J. Wei, H. W. Chung, N. Scales, A. Tanwani, H. Cole-Lewis, S. Pfohl et al. , “Large language models encode clinical knowledge,” arXiv preprint arXiv:2212.13138 , 2022
Original
2022
Later among the works it cites.
A. Alambo, “Suicide risk assessment using reddit,” https://github.com/AmanuelF/Suicide-Risk-Assessment-using-Reddit , 2022
2022
Later among the works it cites.
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg et al. , “Sparks of artificial general intelligence: Early experiments with gpt-4,” arXiv preprint arXiv:2303.12712 , 2023
Original
2023
Closest in time.
H. Nori, N. King, S. M. McKinney, D. Carignan, and E. Horvitz, “Capabilities of gpt-4 on medical challenge problems,” arXiv preprint arXiv:2303.13375 , 2023

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2020

Cited alongside, same era.

Original

2023

Closest in time.