社交媒体中焦虑的早期语言模式：基于可解释语言特征的多维验证研究与作者不相交评估
Early Linguistic Pattern of Anxiety from Social Media Using Interpretable Linguistic Features: A Multi-Faceted Validation Study with Author-Disjoint Evaluation
AI总结：
本文提出了一种基于可解释语言特征的社交媒体焦虑检测方法，通过多维度验证和作者不相交评估，实现了高准确率和鲁棒性。
摘要：
焦虑影响着全球数百万人，但大规模筛查仍然有限。社交媒体语言提供了可扩展检测的机会，但当前模型往往缺乏可解释性、关键词鲁棒性验证和严格用户层面的数据完整性。本文提出了一种透明的方法，通过基于语言特征的特征 grounded 模型和跨领域验证来实现基于社交媒体的焦虑检测。使用大量 Reddit 帖子数据集，我们在精心挑选的子版块上训练了一个逻辑回归分类器用于训练、验证和测试分割。全面的评估包括特征消融、关键词遮蔽实验、以及比较焦虑组和对照组的密度差异分析，以及使用临床访谈参与者进行外部验证。模型在保持高准确率的情况下，即使在情感去除或关键词遮蔽后仍表现出色。使用最小帖子历史的早期检测显著优于随机分类，跨领域分析显示与临床访谈数据有很强的一致性。结果表明，透明的语言特征可以支持可靠、可推广且关键词鲁棒的焦虑检测。所提出的框架为不同在线环境下的可解释心理健康筛查提供了可重复的基准。
英文摘要：
Anxiety affects hundreds of millions of individuals globally, yet large-scale screening remains limited. Social media language provides an opportunity for scalable detection, but current models often lack interpretability, keyword-robustness validation, and rigorous user-level data integrity. This work presents a transparent approach to social media-based anxiety detection through linguistically interpretable feature-grounded modeling and cross-domain validation. Using a substantial dataset of Reddit posts, we trained a logistic regression classifier on carefully curated subreddits for training, validation, and test splits. Comprehensive evaluation included feature ablation, keyword masking experiments, and varying-density difference analyses comparing anxious and control groups, along with external validation using clinically interviewed participants with diagnosed anxiety disorders. The model achieved strong performance while maintaining high accuracy even after sentiment removal or keyword masking. Early detection using minimal post history significantly outperformed random classification, and cross-domain analysis demonstrated strong consistency with clinical interview data. Results indicate that transparent linguistic features can support reliable, generalizable, and keyword-robust anxiety detection. The proposed framework provides a reproducible baseline for interpretable mental health screening across diverse online contexts.
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。
