Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts

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 E arly -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.