Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909769203187712 |
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| author | Blum, Paul Liscio, Enrico Zhang, Ruixuan Figueroa, Caroline Murukannaiah, Pradeep K. |
| author_facet | Blum, Paul Liscio, Enrico Zhang, Ruixuan Figueroa, Caroline Murukannaiah, Pradeep K. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03530 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts Blum, Paul Liscio, Enrico Zhang, Ruixuan Figueroa, Caroline Murukannaiah, Pradeep K. Computation and Language 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. |
| title | Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.03530 |