Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent psychologically meaningful risk factors. In high-stakes mental health applications, understanding these internal representations is essential for safety, transparency, and responsible deployment. In this work, we move beyond accuracy and analyze how suicide detection models trained on original and topic-augmented datasets encode psychological risk factors in their internal representation space. Using visualization and geometric analysis, we examine the coherence and separability of topic-related features. Our results show that topic-aware augmentation increases the clarity and distinctness of underrepresented psychosocial risk factors such as immigration, family issues, and financial crisis. These findings suggest that augmentation not only improves model performance but also leads to more structured and interpretable internal representations.

翻译：自杀意念检测模型通常以整体性能指标进行评估，但对其内部如何表征具有心理学意义的危险因素却知之甚少。在高风险心理健康应用中，理解这些内部表征对于安全性、透明性和负责任的部署至关重要。在本研究中，我们超越准确率，分析了基于原始数据和主题增强数据训练的自杀检测模型，如何在其内部表征空间中编码心理风险因素。通过可视化和几何分析，我们检验了与主题相关特征的一致性和可分性。我们的结果表明，主题感知增强提高了未被充分代表的心理社会风险因素（如移民、家庭问题和财务危机）的清晰度和区分度。这些发现表明，数据增强不仅提升了模型性能，还促成了更结构化、更可解释的内部表征。