Identifying suicide-related language in smartphone keyboard entries among high-risk adolescents. Bloom, P. A., Treves, I. N., Pagliaccio, D., Nadel, I., Wool, E., Quinones, H., Greenblatt, J., Parjane, N., Durham, K., Salem, S., Trivedi, E., Galfalvy, H., Allen, N. B., Barch, D. M., Blanchard, A., Brent, D., Chernick, L. S., Dayan, P. S., Hoyniak, C., Joyce, K., Kirshenbaum, J. S., Li, L. Y., Luby, J., Molina, S., Porta, G., Price, Z., Purvin, E., Rosenberg, A. S., Saha, K., Shankman, S. A., Schwartz, A., Shimgekar, S. R., Zelazny, J., & Auerbach, R. P. npj Digital Medicine, Nature Publishing Group, June, 2026. ![Identifying suicide-related language in smartphone keyboard entries among high-risk adolescents [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Adolescent suicide rates have risen over the past two decades, underscoring the need for improved risk detection strategies. Although natural language processing (NLP) tools are increasingly used to flag suicide-related content, little is known about how such approaches perform on adolescents’ smartphone communications. Addressing this gap, this study leverages passively collected smartphone data to identify suicide-related language in adolescents’ keyboard usage via NLP. We developed a lexicon of suicide-related adolescent language and validated it with labeled data (N = 171,468 text entries; e.g., messages, web searches), demonstrating higher performance in identifying suicide-related text than few-shot prediction with large language models (LLMs) and lexicons not designed for youth. Across two independent cohorts at elevated suicide risk (Ns = 208 & 257; \textgreater6 million text entries), lifetime suicidal thoughts and behaviors (STB) and current suicidal ideation were associated with increased frequency of smartphone suicide-related language. Human coding indicated varied language, including authentic first-person current suicidal ideation (14.5%) and jokes or hyperbole (20.2%). Compared with the lexicon alone, human coding of suicide-related entries with first-person language showed stronger associations with STB history. These findings highlight that effective NLP-based tools for suicide prevention will require more nuanced and context-specific approaches to better distinguish suicidal intent.
@article{bloom_identifying_2026,
title = {Identifying suicide-related language in smartphone keyboard entries among high-risk adolescents},
copyright = {2026 The Author(s)},
issn = {2398-6352},
url = {https://www.nature.com/articles/s41746-026-02921-x},
doi = {10.1038/s41746-026-02921-x},
abstract = {Adolescent suicide rates have risen over the past two decades, underscoring the need for improved risk detection strategies. Although natural language processing (NLP) tools are increasingly used to flag suicide-related content, little is known about how such approaches perform on adolescents’ smartphone communications. Addressing this gap, this study leverages passively collected smartphone data to identify suicide-related language in adolescents’ keyboard usage via NLP. We developed a lexicon of suicide-related adolescent language and validated it with labeled data (N = 171,468 text entries; e.g., messages, web searches), demonstrating higher performance in identifying suicide-related text than few-shot prediction with large language models (LLMs) and lexicons not designed for youth. Across two independent cohorts at elevated suicide risk (Ns = 208 \& 257; {\textgreater}6 million text entries), lifetime suicidal thoughts and behaviors (STB) and current suicidal ideation were associated with increased frequency of smartphone suicide-related language. Human coding indicated varied language, including authentic first-person current suicidal ideation (14.5\%) and jokes or hyperbole (20.2\%). Compared with the lexicon alone, human coding of suicide-related entries with first-person language showed stronger associations with STB history. These findings highlight that effective NLP-based tools for suicide prevention will require more nuanced and context-specific approaches to better distinguish suicidal intent.},
language = {en},
urldate = {2026-06-26},
journal = {npj Digital Medicine},
publisher = {Nature Publishing Group},
author = {Bloom, Paul Alexander and Treves, Isaac N. and Pagliaccio, David and Nadel, Isabella and Wool, Emma and Quinones, Hayley and Greenblatt, Julia and Parjane, Natalia and Durham, Katherine and Salem, Samantha and Trivedi, Esha and Galfalvy, Hanga and Allen, Nicholas B. and Barch, Deanna M. and Blanchard, Ashley and Brent, David and Chernick, Lauren S. and Dayan, Peter S. and Hoyniak, Caroline and Joyce, Karla and Kirshenbaum, Jaclyn S. and Li, Lilian Y. and Luby, Joan and Molina, Simryn and Porta, Giovanna and Price, Zoe and Purvin, Eva and Rosenberg, Alexander Saul and Saha, Koustuv and Shankman, Stewart A. and Schwartz, Adela and Shimgekar, Soorya Ram and Zelazny, Jamie and Auerbach, Randy P.},
month = jun,
year = {2026},
keywords = {Psychology, Medical research, Health care},
}
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