The World Health Organization (WHO) estimated that approximately 1.4 million individuals worldwide died by suicide in 2022. This figure indicates that one person died by suicide every 20 s during the year. Globally, suicide is the tenth-leading cause of death, while it is the second-leading cause of death among young people aged 15329 years. In 2022, it was estimated that approximately 10.5 million suicide attempts would occur. The WHO suggests that along with each completed suicide attempt, many individuals attempt suicide. Today, social media is a place in which people share their feelings. Thus, social media can help us understand the thoughts and possible actions of individuals. This study leverages this advantage and focuses on developing an automated model to use information from social media to determine whether someone is contemplating self-harm. This model is based on the Suicidal-ELECTRA model. We collected datasets of social media posts, processed them, and used them to train and fiune-tune our model. Evaluation of the refined model with a testing dataset consistently yielded outstanding results. The model had an impressive accuracy rate of 93% and commendable F1 score of 0.93. Additionally, we developed an application programming interface that seamlessly integrated our tool with third-party platforms, enhancing its implementation potential to address the concern of rising suicide rates.

翻译：世界卫生组织(WHO)估计，2022年全球约有140万人死于自杀。这一数据表明，当年平均每20秒就有一人死于自杀。在全球范围内，自杀是第十大死因，而在15-29岁年轻人中更是第二大死因。据估计，2022年约有1050万次自杀未遂事件发生。世界卫生组织指出，每一起自杀死亡事件背后，都有许多人曾尝试自杀。如今，社交媒体成为人们分享情感的空间，因此有助于我们理解个人的想法和潜在行为。本研究利用这一优势，致力于开发一个基于Suicidal-ELECTRA模型的自动化系统，通过分析社交媒体信息判断用户是否存在自伤倾向。我们收集了社交媒体帖子数据集并进行预处理，用于模型的训练与微调。经测试数据集评估，优化后的模型始终表现优异，准确率达到93%，F1分数同样为0.93。此外，我们还开发了应用程序编程接口(API)，使该工具能与第三方平台无缝对接，从而增强其在应对自杀率上升问题中的实际应用潜力。