@inproceedings{blum-etal-2026-reading,
title = "Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts",
author = "Blum, Paul and
Liscio, Enrico and
Zhang, Ruixuan and
Figueroa, Caroline and
Murukannaiah, Pradeep K.",
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.31/",
pages = "360--374",
ISBN = "979-8-89176-495-8",
abstract = "Suicide is a leading cause of death, yet predicting it remains a significant challenge. Risk factors such as depression or substance use are commonly used for prediction, but their predictive performance is often only slightly better than chance. Additionally, many cases go undetected due to a lack of contact with mental health services. Social media, however, offers a unique opportunity, as people often share their thoughts and struggles online in real time. In this work, we propose a novel task and method intending to achieve a positive impact through earlier identification: we predict suicidal ideation and behavior (SIB) before a user ever expresses it on an online forum. We introduce Early-SIB, a transformer-based model that sequentially processes the posts a user writes and engages with to predict whether they will write a SIB post. Our model achieves a balanced accuracy of 0.73 in predicting future SIB on a Dutch youth forum, demonstrating that such tools can offer a meaningful addition to traditional methods. Additionally, we employ Shapley Additive Explanations to help human reviewers better understand the model{'}s predictions."
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%0 Conference Proceedings
%T Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts
%A Blum, Paul
%A Liscio, Enrico
%A Zhang, Ruixuan
%A Figueroa, Caroline
%A Murukannaiah, Pradeep K.
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F blum-etal-2026-reading
%X Suicide is a leading cause of death, yet predicting it remains a significant challenge. Risk factors such as depression or substance use are commonly used for prediction, but their predictive performance is often only slightly better than chance. Additionally, many cases go undetected due to a lack of contact with mental health services. Social media, however, offers a unique opportunity, as people often share their thoughts and struggles online in real time. In this work, we propose a novel task and method intending to achieve a positive impact through earlier identification: we predict suicidal ideation and behavior (SIB) before a user ever expresses it on an online forum. We introduce Early-SIB, a transformer-based model that sequentially processes the posts a user writes and engages with to predict whether they will write a SIB post. Our model achieves a balanced accuracy of 0.73 in predicting future SIB on a Dutch youth forum, demonstrating that such tools can offer a meaningful addition to traditional methods. Additionally, we employ Shapley Additive Explanations to help human reviewers better understand the model’s predictions.
%U https://aclanthology.org/2026.nlp4pi-1.31/
%P 360-374
Markdown (Informal)
[Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts](https://aclanthology.org/2026.nlp4pi-1.31/) (Blum et al., NLP4PI 2026)
ACL