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

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 for early identification: predicting 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. Finally, we use Shapley Additive Explanations to make the model's predictions more interpretable.

Computation and Language (cs.CL)

arXiv:2509.03530 [cs.CL]

(or arXiv:2509.03530v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2509.03530 Focus to learn more arXiv-issued DOI via DataCite

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