⏱ 9 min read
References
-
Green, J. G. et al. Childhood adversities and adult psychopathology in the National Comorbidity Survey Replication (NCS-R) I: associations with first onset of DSM-IV disorders. Arch. Gen. Psychiatry 67, 113 (2010).
-
Nelson, C. A. & Gabard-Durnam, L. Early adversity and critical periods: neurodevelopmental consequences of violating the expectable environment. Trends Neurosci. 43, 133–143 (2020).
-
Wade, M., Wright, L. & Finegold, K. E. The effects of early life adversity on children’s mental health and cognitive functioning. Transl. Psychiatry 12, 1–12 (2022).
-
Cahill, S., Hager, R. & Shryane, N. Patterns of resilient functioning in early life: identifying distinct groups and associated factors. Dev. Psychopathol. 36, 1789–1809 (2024).
-
Masten, A. S. Ordinary magic: resilience processes in development. Am. Psychol. 56, 227–238 (2001).
-
Amelio, P. et al. Evaluating the development and well-being assessment (DAWBA) in pediatric anxiety and depression. Child Adolesc. Psychiatry Ment. Health 18, 12 (2024).
-
Jordans, M. J. D. & Kohrt, B. A. Scaling up mental health care and psychosocial support in low-resource settings: a roadmap to impact. Epidemiol. Psychiatr. Sci. 29, e189 (2020).
-
Pennebaker, J. W., Mehl, M. R. & Niederhoffer, K. G. Psychological aspects of natural language use: our words, our selves. Annu. Rev. Psychol. 54, 547–577 (2003).
-
Pennebaker, J. W. & King, L. A. Linguistic styles: language use as an individual difference. J. Pers. Soc. Psychol. 77, 1296–1312 (1999).
-
Chung, C. & Pennebaker, J. Social Communication (Psychology Press, 2007).
-
Rude, S., Gortner, E.-M. & Pennebaker, J. Language use of depressed and depression-vulnerable college students. Cogn. Emot. 18, 1121–1133 (2004).
-
Argaman, O. Linguistic markers and emotional intensity. J. Psycholinguist. Res. 39, 89–99 (2010).
-
Guntuku, S. C., Yaden, D. B., Kern, M. L., Ungar, L. H. & Eichstaedt, J. C. Detecting depression and mental illness on social media: an integrative review. Curr. Opin. Behav. Sci.18, 43–49 (2017).
-
Liu, T. et al. Head versus heart: social media reveals differential language of loneliness from depression. npj Ment. Health Res. 1, 16 (2022).
-
Brockmeyer, T. et al. Me, myself, and I: self-referent word use as an indicator of self-focused attention in relation to depression and anxiety. Front. Psychol. 6, 1564 (2015).
-
Edwards, T. & Holtzman, N. S. A meta-analysis of correlations between depression and first person singular pronoun use. J. Res. Pers. 68, 63–68 (2017).
-
Beech, A. et al. Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic. J. Affect. Disord. 376, 113–121 (2025).
-
Eichstaedt, J. C. et al. Facebook language predicts depression in medical records. Proc. Natl Acad. Sci. USA 115, 11203–11208 (2018).
-
Atkins, D. C., Steyvers, M., Imel, Z. E. & Smyth, P. Scaling up the evaluation of psychotherapy: evaluating motivational interviewing fidelity via statistical text classification. Implement. Sci. 9, 49 (2014).
-
Flemotomos, N. et al. Automated quality assessment of cognitive behavioral therapy sessions through highly contextualized language representations. PLoS ONE 16, e0258639 (2021).
-
Goldberg, S. B. et al. Machine learning and natural language processing in psychotherapy research: alliance as example use case. J. Couns. Psychol. 67, 438–448 (2020).
-
Malins, S. et al. Developing an automated assessment of in-session patient activation for psychological therapy: codevelopment approach. JMIR Med. Inform. 10, e38168 (2022).
-
Ewbank, M. P. et al. Quantifying the association between psychotherapy content and clinical outcomes using deep learning. JAMA Psychiatry 77, 35–43 (2020).
-
Nook, E. C., Hull, T. D., Nock, M. K. & Somerville, L. H. Linguistic measures of psychological distance track symptom levels and treatment outcomes in a large set of psychotherapy transcripts. Proc. Natl Acad. Sci. USA 119, e2114737119 (2022).
-
He, Q., Veldkamp, B. P., Glas, C. A. W. & de Vries, T. Automated assessment of patients’ self-narratives for posttraumatic stress disorder screening using natural language processing and text mining. Assessment 24, 157–172 (2017).
-
Schultebraucks, K., Yadav, V., Shalev, A. Y., Bonanno, G. A. & Galatzer-Levy, I. R. Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood. Psychol. Med. 52, 957–967 (2022).
-
Son, Y. et al. World Trade Center responders in their own words: predicting PTSD symptom trajectories with AI-based language analyses of interviews. Psychol. Med. 53, 918–926 (2023).
-
Pestian, J. P. et al. A controlled trial using natural language processing to examine the language of suicidal adolescents in the emergency department. Suicide Life Threat. Behav. 46, 154–159 (2016).
-
Venek, V., Scherer, S., Morency, L.-P., Rizzo, A. S. & Pestian, J. Adolescent suicidal risk assessment in clinician–patient interaction. IEEE Trans. Affect. Comput. 8, 204–215 (2017).
-
Weintraub, M. J., Posta, F., Ichinose, M. C., Arevian, A. C. & Miklowitz, D. J. Word usage in spontaneous speech as a predictor of depressive symptoms among youth at high risk for mood disorders. J. Affect. Disord. 323, 675–678 (2023).
-
Asarnow, J. R., Goldstein, M. J., Tompson, M. & Guthrie, D. One-year outcomes of depressive disorders in child psychiatric in-patients: evaluation of the prognostic power of a brief measure of expressed emotion. J. Child Psychol. Psychiatry 34, 129–137 (1993).
-
Burkhouse, K. L., Uhrlass, D. J., Stone, L. B., Knopik, V. S. & Gibb, B. E. Expressed emotion-criticism and risk of depression onset in children. J. Clin. Child Adolesc. Psychol. 41, 771–777 (2012).
-
Levis, M., Westgate, C. L., Gui, J., Watts, B. V. & Shiner, B. Natural language processing of clinical mental health notes may add predictive value to existing suicide risk models. Psychol. Med. 51, 1382–1391 (2021).
-
Antonacci, C. et al. Frontolimbic connectivity and threat-related psychopathology: a data-driven test of models of early adversity. Dev. Psychobiol. 67, e70080 (2025).
-
Hou, J., Mortel, L., Popma, A., Smit, D. & van Wingen, G. Predicting the onset of mental health problems in adolescents. Psychol. Med. 55, e128 (2025).
-
Bufano, P., Laurino, M., Said, S., Tognetti, A. & Menicucci, D. Digital phenotyping for monitoring mental disorders: systematic review. J. Med. Internet Res. 25, e46778 (2023).
-
Kliamovich, D. et al. Leveraging distributed brain signal at rest to predict internalizing symptoms in youth. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 10, 58–67 (2025).
-
Danese, A. & Widom, C. S. Objective and subjective experiences of child maltreatment and their relationships with psychopathology. Nat. Hum. Behav. 4, 811–818 (2020).
-
Hill, E. D. et al. Prediction of mental health risk in adolescents. Nat. Med. 31, 1840–1846 (2025).
-
Vandewouw, M. M. et al. Using deep learning to predict internalizing problems from brain structure in youth. Transl. Psychiatry 15, 326 (2025).
-
Chng, S. Y. et al. Ethical considerations in AI for child health and recommendations for child-centered medical AI. npj Digit. Med. 8, 152 (2025).
-
Fusar-Poli, P. et al. Ethical considerations for precision psychiatry: a roadmap for research and clinical practice. Eur. Neuropsychopharmacol. 63, 17–34 (2022).
-
Ribbe, D. in Measurement of Stress, Trauma, and Adaptation (ed. Stamm, B. H.) 386–387 (Sidran, 1996).
-
Rudolph, K. D. et al. Toward an interpersonal life-stress model of depression: the developmental context of stress generation. Dev. Psychopathol. 12, 215–234 (2000).
-
Achenbach, T. M. & Rescorla, L. A. Manual for the ASEBA School-Age Forms and Profiles: An Integrated System of Multi-Informant Assessment (ASEBA, 2001).
-
Ridge, N. W., Warren, J. S., Burlingame, G. M., Wells, M. G. & Tumblin, K. M. Reliability and validity of the youth outcome questionnaire self-report. J. Clin. Psychol. 65, 1115–1126 (2009).
-
Kaufman, J. & Schweder, A. E. in Comprehensive Handbook of Psychological Assessment, Vol. 2: Personality Assessment 247–255 (Wiley, 2004).
-
Copeland, W., Shanahan, L., Costello, E. J. & Angold, A. Cumulative prevalence of psychiatric disorders by young adulthood: a prospective cohort analysis from the Great Smoky Mountains Study. J. Am. Acad. Child Adolesc. Psychiatry 50, 252–261 (2011).
-
Radford, A. et al. Robust speech recognition via large-scale weak supervision. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 28492–28518 (PMLR, 2023).
-
Baevski, A., Zhou, Y., Mohamed, A. & Auli, M. wav2vec 2.0: a framework for self-supervised learning of speech representations. In Advances in Neural Information Processing Systems 12449–12460 (Curran Associates, 2020).
-
pyannote/speaker-diarization-3.1. Hugging Face www.huggingface.co/pyannote/speaker-diarization-3.1 (2024).
-
Seedat, S. et al. Cross-national associations between gender and mental disorders in the WHO World Mental Health Surveys. Arch. Gen. Psychiatry 66, 785–795 (2009).
-
Solmi, M. et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol. Psychiatry 27, 281–295 (2022).
-
Boyd, R. L., Ashokkumar, A., Seraj, S. & Pennebaker, J. W. The Development and Psychometric Properties of LIWC-22 (Univ. Texas at Austin, 2022); https://doi.org/10.13140/RG.2.2.23890.43205
-
Salton, G. & Buckley, C. Term-weighting approaches in automatic text retrieval. Inf. Proc. Manag. 24, 513–523 (1988).
-
Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003).
-
Schwartz, H. A. et al. DLATK: differential language analysis toolkit. In Proc. 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations (eds Specia, L. et al.) 55–60 (Association for Computational Linguistics, 2017); https://doi.org/10.18653/v1/D17-2010
-
McCallum, A. K. MALLET a machine learning for language toolkit. Sci. Res. https://www.scirp.org/reference/referencespapers?referenceid=490739 (2002).
-
Eichstaedt, J. C. et al. Closed- and open-vocabulary approaches to text analysis: a review, quantitative comparison, and recommendations. Psychol. Methods 26, 398–427 (2021).
-
Schwartz, H. A. et al. Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8, e73791 (2013).
-
Argamon, S., Koppel, M., Pennebaker, J. W. & Schler, J. Mining the blogosphere: age, gender and the varieties of self-expression. First Monday https://doi.org/10.5210/fm.v12i9.2003 (2007).
-
Huertas-García, Á., Martín, A., Huertas-Tato, J. & Camacho, D. Exploring dimensionality reduction techniques in multilingual transformers. Cogn. Comput. 15, 590–612 (2023).
-
Kern, M. L. et al. Gaining insights from social media language: methodologies and challenges. Psychol. Methods 21, 507–525 (2016).
-
Liu, Y. et al. RoBERTa: a robustly optimized BERT pretraining approach. Preprint at https://arxiv.org/abs/1907.11692 (2019).
-
RoBERTa. Hugging Face https://huggingface.co/docs/transformers/en/model_doc/roberta (2020).
-
López-Otal, M. et al. Linguistic interpretability of transformer-based language models: a systematic review. Preprint at https://arxiv.org/abs/2504.08001v1 (2025).
-
Vaswani, A. et al. Attention is all you need. Preprint at https://arxiv.org/abs/1706.03762 (2023).

Leave a Reply