MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare
Open accessAbstract
Mental health is a critical issue in modern society, and mental disorders could sometimes turn to suicidal ideation without adequate treatment.Early detection of mental disorders and suicidal ideation from social content provides a potential way for effective social intervention.Recent advances in pretrained contextualized language representations have promoted the development of several domain-specific pretrained models and facilitated several downstream applications.However, there are no existing pretrained language models for mental healthcare.This paper trains and releases two pretrained masked language models, i.e., MentalBERT and MentalRoBERTa, to benefit machine learning for the mental healthcare research community.Besides, we evaluate our trained domain-specific models and several variants of pretrained language models on several mental disorder detection benchmarks and demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515 · see citing works
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218 · see citing works
Domain-specific language model pretraining for biomedical natural language processing
Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3458754 · Cited by 2,170 · see citing works
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.740 · Cited by 2,103 · see citing works
Alsentzer, E., Murphy, J., Boag, W., Weng, W.-H., Jindi, D., Naumann, T., & McDermott, M. (2019) · Proceedings of the 2nd Clinical Natural Language Processing Workshop · doi:10.18653/v1/w19-1909 · Cited by 1,736 · see citing works
Machine learning in mental health: a scoping review of methods and applications
Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019) · Psychological Medicine · doi:10.1017/s0033291719000151 · Cited by 1,047 · see citing works
Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media
Choudhury, M. D., Kıcıman, E., Dredze, M. H., Coppersmith, G., & Kumar, M. (2016) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858207 · Cited by 682 · see citing works
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Huang, K., Altosaar, J., & Ranganath, R. (2019) · arXiv · doi:10.48550/arxiv.1904.05342 · Cited by 618 · see citing works
Detection of Depression-Related Posts in Reddit Social Media Forum
Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2909180 · Cited by 466 · see citing works
Natural language processing in mental health applications using non-clinical texts
Calvo, R. A., Milne, D., Hussain, M. S., & Christensen, H. M. (2017) · Natural Language Engineering · doi:10.1017/s1351324916000383 · Cited by 383 · see citing works
Deep learning in mental health outcome research: a scoping review
Su, C., Xu, Z., Pathak, J., & Wang, F. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0780-3 · Cited by 368 · see citing works
CLPsych 2015 Shared Task: Depression and PTSD on Twitter
Coppersmith, G., Dredze, M. H., Harman, C., Hollingshead, K., & Mitchell, M. (2015) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.3115/v1/w15-1204 · Cited by 329 · see citing works
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
Ji, S., Pan, S., Li, X., Cambria, E., Long, G., & Huang, Z. H. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.3021467 · Cited by 275 · see citing works
Self-citationA Test Collection for Research on Depression and Language Use
Losada, D. E., & Crestani, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-44564-9_3 · Cited by 244 · see citing works
Supervised Learning for Suicidal Ideation Detection in Online User Content
Ji, S., Yu, C. P., Fung, S.-F., Pan, S., & Long, G. (2018) · Complexity · doi:10.1155/2018/6157249 · Cited by 215 · see citing works
Self-citationExpert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
Shing, H.-C., Nair, S., Zirikly, A., Friedenberg, M., Daumé, H., & Resnik, P. (2018) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/w18-0603 · Cited by 214 · see citing works
Application of Machine Learning Methods in Mental Health Detection: A Systematic Review
Rahman, R. A., Omar, K., Noah, S. A. M., Danuri, M. S. N. M., & Al-Garadi, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.3029154 · Cited by 189 · see citing works
Dreaddit: A Reddit Dataset for Stress Analysis in Social Media
Turcan, E., & McKeown, K. (2019) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/d19-6213 · Cited by 156 · see citing works
Windfuhr, K. L., & Kapur, N. (2011) · British Medical Bulletin · doi:10.1093/bmb/ldr042 · Cited by 146 · see citing works
Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction
Alt, C., Hübner, M., & Hennig, L. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1134 · Cited by 131 · see citing works
Suicidal ideation and mental disorder detection with attentive relation networks
Ji, S., Li, X., Huang, Z. H., & Cambria, E. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06208-y · Cited by 131 · see citing works
Self-citationIdentifying Depression on Reddit: The Effect of Training Data
Pirina, I., & Çöltekin, Ç. (2018) · Social Media Mining for Health Research and Applications Workshop and Shared Tasks (SMM4H) · doi:10.18653/v1/w18-5903 · Cited by 121 · see citing works
CNN-based ranking for biomedical entity normalization
Li, H., Chen, Q., Tang, B., Wang, X., Xu, H., Wang, B., & Huang, D. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1805-7 · Cited by 119 · see citing works
Using Topic Modeling to Improve Prediction of Neuroticism and Depression in College Students
Resnik, P., Garron, A., & Resnik, R. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1133 · Cited by 90 · see citing works
Fine-Tuning BERT for Multi-Label Sentiment Analysis in Unbalanced Code-Switching Text
Tang, T., Tang, X., & Yuan, T. (2020) · IEEE Access · doi:10.1109/access.2020.3030468 · Cited by 88 · see citing works
Lin, C., Miller, T. A., Dligach, D., Bethard, S. J., & Savova, G. (2019) · Clinical Natural Language Processing Workshop (ClinicalNLP) · doi:10.18653/v1/w19-1908 · Cited by 84 · see citing works
Yoo, M., Lee, S., & Ha, T. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.10.001 · Cited by 83 · see citing works
Detection of Mental Health from Reddit via Deep Contextualized Representations
Jiang, Z., Levitan, S. I., Zomick, J., & Hirschberg, J. (2020) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/2020.louhi-1.16 · Cited by 80 · see citing works
Song, Y., Wang, J., Liang, Z., Liu, Z., & Jiang, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04815 · Cited by 78 · see citing works
Rao, G., Zhang, Y., Zhang, L., Cong, Q., & Feng, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2973737 · Cited by 72 · see citing works
Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals
Kang, M., Shin, S., Jung, J., & Kim, Y. T. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/9951905 · Cited by 59 · see citing works
Recurrent Neural Network (RNN) to Analyse Mental Behaviour in Social Media
Bouarara, H. A. (2021) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.2021070101 · Cited by 58 · see citing works
Mauriello, M. L., Lincoln, E. T., Hon, G., Simon, D., Jurafsky, D., & Paredes, P. E. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451799 · Cited by 38 · see citing works
Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERTPreprint
Bucur, A.-M., Cosma, A., & Dinu, L. P. (2021) · arXiv (Cornell University) · doi:10.13140/rg.2.2.25060.50567 · Cited by 34 · see citing works
Surveying Machine Learning Algorithms On Eeg Signals Data For Mental Health Assessment
Gore, E., & Rathi, S. (2019) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon46936.2019.9105749 · Cited by 15 · see citing works
NLP-UNED at eRisk 2021: self-harm early risk detection with TF-IDF and linguistic features.
Ageitos, E. C., Fabregat, H., Araujo, L., & Martínez-Romo, J. (2021) · CLEF (Working Notes) · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 11
- Ansari, L., Ji, S., Chen, Q., and Cambria, E. (2022). Ensemble hybrid learning methods for automated depression detection. IEEE Transactions on Com- putational Social Systems.
- Cambria, E., Hussain, A., Durrani, T., Havasi, C., Eckl, C., and Munro, J. (2010). Sentic computing for patient centered applications. In IEEE ICSP, pages 1279-1282.
- Cambria, E., Benson, T., Eckl, C., and Hussain, A. (2012). Sentic PROMs: Application of sentic com- puting to the development of a novel unified frame- work for measuring health-care quality. Expert Sys- tems with Applications, 39(12):10533-10543.
- Cambria, E., Liu, Q., Decherchi, S., Xing, F., , and Kwok, K. (2022). SenticNet 7: A commonsense- based neurosymbolic ai framework for explainable sentiment analysis. In LREC.
- Grassi, M., Cambria, E., Hussain, A., and Piazza, F. (2011). Sentic web: A new paradigm for managing social media affective information. Cognitive Com- putation, 3(3):480-489.
- Lee, A., Kummerfeld, J. K., An, L. C., and Mihalcea, R. (2021). Micromodels for efficient, explainable, and reusable systems: A case study on mental health. arXiv preprint arXiv:2109.13770. · arXiv
- Lin, C., Miller, T., Dligach, D., Bethard, S., and Savova, G. (2019). A BERT-based universal model for both within-and cross-sentence clinical temporal relation extraction. In Proceedings of the 2nd Clin- ical Natural Language Processing Workshop, pages 65-71.
- Martínez-Castaño, R., Htait, A., Azzopardi, L., and Moshfeghi, Y. (2021). BERT-Based Transform- ers for Early Detection of Mental Health Illnesses. In International Conference of the Cross-Language Evaluation Forum for European Languages, pages 189-200. Springer.
- Mehta, Y., Majumder, N., Gelbukh, A., and Cambria, E. (2020). Recent trends in deep learning based personality detection. Artificial Intelligence Review, 53:2313-2339.
- Shickel, B., Siegel, S., Heesacker, M., Benton, S., and Rashidi, P. (2020). Automatic detection and clas- sification of cognitive distortions in mental health text. In 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), pages 275-280. IEEE.
- Wolf, T., Chaumond, J., Debut, L., Sanh, V., Delangue, C., Moi, A., Cistac, P., Funtowicz, M., Davison, J., Shleifer, S., et al. (2020). Transformers: State-of- the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstra- tions, pages 38-45.
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Lawrence, H. R., Schneider, R. A., Rubin, S. B., Matarić, M. J., McDuff, D. J., & Jones Bell, M. (2024) · JMIR Mental Health · doi:10.2196/59479 · Cited by 235 · see citing works
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Chen, Z. S., Kulkarni, P., Galatzer‐Levy, I. R., Bigio, B., Nasca, C., & Zhang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100602 · Cited by 225 · see citing works
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Yang, K., Ji, S., Zhang, T., Xie, Q., Kuang, Z., & Ananiadou, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.370 · Cited by 155 · see citing works
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Garg, M. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111228 · Cited by 21 · see citing works
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Qin, R., Yang, K., Abbasi, A., Dobolyi, D. G., Seyedi, S. S., Griner, E. L., Kwon, H., Cotes, R. O., Jiang, Z., Clifford, G. D., et al. (2024) · ACM Transactions on Management Information Systems · doi:10.1145/3673906 · Cited by 18 · see citing works
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Shi, J., Wang, Z., Zhou, J., Liu, C., Sun, P. Z. H., Zhao, E., & Lü, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3594133 · Cited by 19 · see citing works
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Mallikarjuna, C., & Sangeetha, S. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103094 · Cited by 18 · see citing works
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Liang, Y., Liu, L., Ji, Y., Huangfu, L., & Zeng, D. D. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103407 · Cited by 19 · see citing works
Zantvoort, K., Scharfenberger, J., Boß, L., Lehr, D., & Funk, B. (2023) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-023-00148-z · Cited by 19 · see citing works
Garg, M., Shahbandegan, A., Chadha, A., & Mago, V. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.757 · Cited by 17 · see citing works
Language model and its interpretability in biomedicine: A scoping review
Lyu, D., Wang, X., Chen, Y., & Wang, F. (2024) · iScience · doi:10.1016/j.isci.2024.109334 · Cited by 17 · see citing works
Zhang, T., Yang, K., & Ananiadou, S. (2023) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2023.bionlp-1.9 · Cited by 15 · see citing works
Self-citationRobust language-based mental health assessments in time and space through social media
Mangalik, S., Eichstaedt, J. C., Giorgi, S., Mun, J., Ahmed, F., Gill, G., Ganesan, A. V., Subrahmanya, S., Soni, N., Clouston, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01100-0 · Cited by 18 · see citing works
Kjell, O., Kjell, K., & Schwartz, H. A. (2023) · doi:10.31234/osf.io/yfd8g · Cited by 15 · see citing works
Kulkarni, D. S., Ghosh, A., Girdhari, A., Liu, S., Vance, L. A., Unruh, M., & Sarkar, J. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100045 · Cited by 14 · see citing works
Kharitonova, K., Pérez‐Fernández, D., Gutiérrez-Hernando, J., Gutiérrez-Fandiño, A., Callejas, Z., & Griol, D. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2321959 · Cited by 14 · see citing works
Are You Depressed? Analyze User Utterances to Detect Depressive Emotions Using DistilBERT
Oh, J., Kim, M., Park, H., & Oh, H. (2023) · Applied Sciences · doi:10.3390/app13106223 · Cited by 14 · see citing works
On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook
Bucur, A.-M., Moldovan, A.-C., Parvatikar, K., Zampieri, M., KhudaBukhsh, A. R., & Dinu, L. P. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3540507 · Cited by 15 · see citing works
Zhang, Z., Li, Z., Zhu, J., Guo, Z., Shi, B., & Hu, B. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111650 · Cited by 13 · see citing works
Khowaja, S. A., Nkenyereye, L., Khuwaja, P., Hamadi, H. A., & Dev, K. (2024) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2024.3461776 · Cited by 13 · see citing works
MIRoBERTa: Mental Illness Text Classification With Transfer Learning on Subreddits
Sao, M., & Lim, H. (2024) · IEEE Access · doi:10.1109/access.2024.3522465 · Cited by 12 · see citing works
A Survey of Large Language Models in Mental Health Disorder Detection on Social Media
Ge, Z., Hu, N., Li, D., Wang, Y., Qi, S., Xu, Y., Han, S., & Zhang, J. Y. (2025) · IEEE International Conference on Data Engineering Workshops (ICDEW) · doi:10.1109/icdew67478.2025.00027 · Cited by 12 · see citing works
A conversational agent framework for mental health screening: design, implementation, and usability
Boian, R., Bucur, A.-M., Todea, D., Luca, A., Rebedea, T. E., & Podina, I. R. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2332934 · Cited by 11 · see citing works
Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts
Maddela, M., Ung, M., Xu, J., Madotto, A., Foran, H., & Boureau, Y.-L. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.763 · Cited by 11 · see citing works
Jain, M., Jain, S. K., Jain, A., & Garg, B. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3402689 · Cited by 12 · see citing works
Saleem, M. T., & Kim, J. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2156 · Cited by 12 · see citing works
Muñoz, S., & Iglesias, C. Á. (2023) · Applied Sciences · doi:10.3390/app132111695 · Cited by 10 · see citing works
Pre- Trained Language Models for Mental Health: An Empirical Study on Arabic Q&A Classification
Alhuzali, H., & Alasmari, A. (2025) · Healthcare · doi:10.3390/healthcare13090985 · Cited by 14 · see citing works
Enhancing TextGCN for depression detection on social media with emotion representation
Mao, H., & Qing, H. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1612769 · Cited by 13 · see citing works
Lybarger, K., Tauscher, J. S., Ding, X., Ben‐Zeev, D., & Cohen, T. (2022) · Workshop on Computational Linguistics and Clinical Psychology (CLPsych) · doi:10.18653/v1/2022.clpsych-1.11 · Cited by 9 · see citing works
Zanwar, S., Li, X., Wiechmann, D., Qiao, Y., & Kerz, E. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.568 · Cited by 8 · see citing works
Aich, A., Quynh, A., Badal, V. D., Pinkham, A., Harvey, P., Depp, C., & Parde, N. (2022) · Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.findings-emnlp.208 · Cited by 12 · see citing works
Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts
Chen, S., Zhang, Z., Wu, M., & Zhu, K. Q. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.562 · Cited by 8 · see citing works
MentalRoBERTa-Caps: A capsule-enhanced transformer model for mental health classification
Wagay, F. A., Jahiruddin, & Altaf, Y. (2025) · MethodsX · doi:10.1016/j.mex.2025.103483 · Cited by 8 · see citing works
MultiWD: Multi-label wellness dimensions in social media posts
Garg, M., Liu, X., Sathvik, M., Raza, S., & Sohn, S. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104586 · Cited by 8 · see citing works
Maternal mental health monitoring in an online community: a natural language processing approach
Zhu, Z. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2333927 · Cited by 8 · see citing works
Enhancing Mental Health Condition Detection on Social Media Through Multi-Task LearningPreprint
Liu, J., & Su, M. (2024) · medRxiv · doi:10.1101/2024.02.23.24303303 · Cited by 8 · see citing works
Burbano, R. N., Rendón, O. M. C., & Astudillo, C. A. (2025) · Applied Sciences · doi:10.3390/app15063358 · Cited by 9 · see citing works
Diagnosis and Classification of Depressive Disorders using ML and DL Models
Bhavani, B. H., Sreenatha, M., & Kundur, N. C. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.10017 · Cited by 7 · see citing works
Kho, J. J., Song, S., Tan, S. M. X., Fitriyah, N. H., Lokadjaja, M. C., Yee, J. Y., Yang, Z., Chen, E. Y. H., Lee, J., & Goh, W. W. B. (2025) · Schizophrenia · doi:10.1038/s41537-025-00649-3 · Cited by 7 · see citing works
Adapting language models for mental health analysis on social media
Aragón, M. E., López-Monroy, A. P., Montes-y-Gómez, M., & Losada, D. E. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103217 · Cited by 9 · see citing works
Values, Ethics, Morals? On the Use of Moral Concepts in NLP Research
Vida, K., Simon, J., & Lauscher, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.368 · Cited by 9 · see citing works
A Comparative Study of Deep Learning Models for Detecting Depressive Disorder in Tweets
(2024) · Journal of System and Management Sciences · doi:10.33168/jsms.2024.0318 · Cited by 8 · see citing works
Luna-Jiménez, C., Gil-Martín, M., D’Haro, L. F., Fernández-Martínez, F., & San-Segundo, R. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124524 · Cited by 7 · see citing works
Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing
Ji, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.297 · Cited by 7 · see citing works
Self-citationLLM-based classifiers for discovering mental disorders
Nowacki, A., Sitek, W., & Rybiński, H. (2025) · Journal of Intelligent Information Systems · doi:10.1007/s10844-025-00934-8 · Cited by 6 · see citing works
Therapy as an NLP Task: Comparing LLMs and Human Peers Behaviors in CBT Sessions
Iftikhar, Z., Ransom, S., Xiao, A. W., Nugent, N. R., & Huang, J. J. S. (2026) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3817112 · Cited by 24 · see citing works
Towards Identifying Fine-Grained Depression Symptoms from Memes
Yadav, S., Caragea, C., Zhao, C., Kumari, N. P., Solberg, M. A., & Sharma, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.495 · Cited by 6 · see citing works
Liu, Z., Wu, Y., Zhang, H., Li, G., Ding, Z., & Hu, B. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3393244 · Cited by 6 · see citing works
Roberta and BERT: Revolutionizing Mental Healthcare through Natural Language
Singla, A. (2024) · doi:10.36676/ssjaiml.v1.i1.02 · Cited by 6 · see citing works
Reliability Analysis of Psychological Concept Extraction and Classification in User-Penned Text
Garg, M., Sathvik, M., Raza, S., Chadha, A., & Sohn, S. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31324 · Cited by 6 · see citing works
Farruque, N., Zai͏̈ane, O. R., Goebel, R. G., & Sivapalan, S. (2022) · Workshop on Language Technology for Equality, Diversity, Inclusion (LT-EDI) · doi:10.18653/v1/2022.ltedi-1.21 · Cited by 6 · see citing works
Dixit, K. K., Pundir, S., Shrivastava, A., Kumar, C. P., Srivastava, A. P., & Singh, P. (2023) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon59197.2023.10434291 · Cited by 6 · see citing works
Kaushik, P., Bansal, K., Kumar, Y., & Changela, A. (2024) · SN Computer Science · doi:10.1007/s42979-024-03416-w · Cited by 5 · see citing works
Priyadarshana, Y. H. P. P., Liang, Z., & Piumarta, I. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1564828 · Cited by 5 · see citing works
Studying the Influence of Toxicity and Emotion Features for Stress Detection on Social Media
Alghamdi, Z., Kumarage, T., Karami, M., Alatawi, F., Mosallanezhad, A., & Liu, H. (2023) · European Conference on Social Media · doi:10.34190/ecsm.10.1.1028 · Cited by 5 · see citing works
SMILE: Evaluation and Domain Adaptation for Social Media Language Understanding
Bashlovkina, V., Matthews, R., Kuang, Z., Baumgartner, S., & Bendersky, M. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599907 · Cited by 6 · see citing works
- Authors
- Ji, S., Zhang, T., Ansari, L., Fu, J., Tiwari, P., & Cambria, E.
- Affiliations
- Aalto University (FI)
- University of Manchester (GB)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Nanyang Technological University (SG)
- Venue
- Proceedings of the Language Resources and Evaluation Conference
- Published
- 2022-06
- Pages
- 7184-7190
- ISSN
- 2522-2686
- OpenAlex
- W3209409148
- Cited by
- 233
- References
- 38
- Citations by year
- 2026: 68 · 2025: 30 · 2024: 62 · 2023: 55 · 2022: 18
- FWCI
- 79.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Language Resources and Evaluation Conference (LREC) (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- China Scholarship Council
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- pre-trained language models, mental disorder detection, suicidal ideation detection, masked language models, social media text analysis, mental health services
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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