Suicide is a leading cause of death among adolescents (aged 12–18), yet predicting it remains a significant challenge. Many cases go undetected because young people often do not contact mental health services. In contrast, young people often share their thoughts and struggles online in real time. To utilize this communication channel, we propose a novel task and method: predicting suicidal ideation and behavior (SIB) from online forums before an adolescent explicitly expresses suicidal ideation on a forum. This predictive framing, where selfdisclosure is not used as input at any stage, is largely unexplored in the suicide prediction literature. 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.

The authors have declared no competing interest.

This material is produced as part of AlgoSoc, a collaborative 10-year research program on public values in the algorithmic society, and the Hybrid Intelligence Centre, both funded by the Dutch Ministry of Education, Culture and Science (OCW) under the Gravitation programme (project numbers 024.005.017 and 024.004.022). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of OCW or those of the AlgoSoc consortium as a whole.

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