标题： RSD-15K：用于社交媒体自杀风险检测的大规模用户级注释数据集 显示英文标题

标题： RSD-15K: A Large-Scale User-Level Annotated Dataset for Suicide Risk Detection on Social Media

摘要： 近年来，认知和心理健康（CMH）障碍日益成为全球公共卫生的重要挑战，特别是由于社会竞争、经济压力和年轻人及中年人之间的人际关系等多方面因素导致的自杀问题。 社交媒体作为个人表达情绪和寻求帮助的重要平台，为自杀风险的早期检测和干预提供了可能性。 本文介绍了一个包含15000条用户级帖子的大规模数据集。 与现有数据集相比，该数据集保留了完整的用户发帖时间序列信息，支持对自杀风险动态演变进行建模，并且我们还对这些数据集进行了全面而严格的标注。 在基准实验中，我们系统评估了传统机器学习方法、深度学习模型和微调大语言模型的性能。 实验结果表明，我们的数据集可以有效支持自杀风险的自动评估任务。 考虑到心理健康数据的敏感性，我们还讨论了数据集的隐私保护和伦理使用问题。 此外，我们还探索了该数据集在心理健康测试、临床精神科辅助治疗等领域的潜在应用，并为未来的研究工作提供了方向性建议。 显示英文摘要

摘要： In recent years, cognitive and mental health (CMH) disorders have increasingly become an important challenge for global public health, especially the suicide problem caused by multiple factors such as social competition, economic pressure and interpersonal relationships among young and middle-aged people. Social media, as an important platform for individuals to express emotions and seek help, provides the possibility for early detection and intervention of suicide risk. This paper introduces a large-scale dataset containing 15,000 user-level posts. Compared with existing datasets, this dataset retains complete user posting time sequence information, supports modeling the dynamic evolution of suicide risk, and we have also conducted comprehensive and rigorous annotations on these datasets. In the benchmark experiment, we systematically evaluated the performance of traditional machine learning methods, deep learning models, and fine-tuned large language models. The experimental results show that our dataset can effectively support the automatic assessment task of suicide risk. Considering the sensitivity of mental health data, we also discussed the privacy protection and ethical use of the dataset. In addition, we also explored the potential applications of the dataset in mental health testing, clinical psychiatric auxiliary treatment, etc., and provided directional suggestions for future research work.