multiMentalRoBERTa：一种用于心理健康障碍的微调多类分类器
multiMentalRoBERTa: A Fine-tuned Multiclass Classifier for Mental Health Disorder
AI总结：
本文提出微调RoBERTa模型multiMentalRoBERTa，对压力、焦虑、抑郁、PTSD、自杀意念和中性话语进行多类分类，在六类与五类设置中宏F1分别达0.839和0.870，优于传统方法、领域transformer及大语言模型，并结合可解释性方法识别关键词汇线索。
摘要：
从社交媒体文本中早期检测心理健康障碍对于实现及时支持、风险评估和转介至适当资源至关重要。本研究引入了 multiMentalRoBERTa，这是一个经过微调的 RoBERTa 模型，用于对常见心理健康状况进行多类分类，包括压力、焦虑、抑郁、创伤后应激障碍（PTSD）、自杀意念和中性话语。基于多个经过整理的数据集，进行了数据探索以分析类别重叠情况，揭示了抑郁与自杀意念以及焦虑与 PTSD 之间的强相关性，而压力则表现为一个宽泛且重叠的类别。与传统机器学习方法、领域特定 transformer 以及基于提示的大语言模型的对比实验表明，multiMentalRoBERTa 取得了优越的性能，在六类设置中宏 F1 分数为 0.839，在五类设置（排除压力）中为 0.870，优于微调后的 MentalBERT 和基线分类器。除了预测准确性之外，还应用了可解释性方法，包括层级积分梯度和 KeyBERT，以识别驱动分类的词汇线索，特别关注区分抑郁与自杀意念。研究结果强调了微调 transformer 在敏感情境下进行可靠且可解释检测的有效性，同时也凸显了公平性、偏见缓解以及人在回路安全协议的重要性。总体而言，multiMentalRoBERTa 被呈现为一种轻量级、稳健且可部署的解决方案，用于增强心理健康平台的支持能力。
英文摘要：
The early detection of mental health disorders from social media text is critical for enabling timely support, risk assessment, and referral to appropriate resources. This work introduces multiMentalRoBERTa, a fine-tuned RoBERTa model designed for multiclass classification of common mental health conditions, including stress, anxiety, depression, post-traumatic stress disorder (PTSD), suicidal ideation, and neutral discourse. Drawing on multiple curated datasets, data exploration is conducted to analyze class overlaps, revealing strong correlations between depression and suicidal ideation as well as anxiety and PTSD, while stress emerges as a broad, overlapping category. Comparative experiments with traditional machine learning methods, domain-specific transformers, and prompting-based large language models demonstrate that multiMentalRoBERTa achieves superior performance, with macro F1-scores of 0.839 in the six-class setup and 0.870 in the five-class setup (excluding stress), outperforming both fine-tuned MentalBERT and baseline classifiers. Beyond predictive accuracy, explainability methods, including Layer Integrated Gradients and KeyBERT, are applied to identify lexical cues that drive classification, with a particular focus on distinguishing depression from suicidal ideation. The findings emphasize the effectiveness of fine-tuned transformers for reliable and interpretable detection in sensitive contexts, while also underscoring the importance of fairness, bias mitigation, and human-in-the-loop safety protocols. Overall, multiMentalRoBERTa is presented as a lightweight, robust, and deployable solution for enhancing support in mental health platforms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
