Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts
This addresses the problem of undetected suicide risk in adolescents who lack mental health contact, offering a potential tool for early intervention, though it is an incremental advance in suicide prediction.
The paper tackled the challenge of predicting suicidal ideation and behavior (SIB) in adolescents by analyzing their social media posts before explicit self-disclosure, achieving a balanced accuracy of 0.73 on a Dutch youth forum.
Suicide is a leading cause of death among adolescents (12-18), yet predicting it remains a significant challenge. Many cases go undetected due to a lack of contact with mental health services. Social media, however, offers a unique opportunity, as young people often share their thoughts and struggles online in real time. In this work, we propose a novel task and method to approach it: predicting suicidal ideation and behavior (SIB) from forum posts before an adolescent explicitly expresses suicidal ideation on an online forum. This predictive framing, where no self-disclosure is used as input at any stage, remains largely unexplored in the suicide prediction literature. To this end, 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 for predicting future SIB on a Dutch youth forum, demonstrating that such tools can offer a meaningful addition to traditional methods.