English(EN) Cognitive-Linguistic Indicators of Depression in Online Communities: Analysed by DistilBERT and Holographic Reduced Representation

AI模型利用认知语言特征提升抑郁症检测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-02 04:00

研究人员开发了一种混合模型，结合了DistilBERT嵌入和认知语言特征来检测在线文本中的抑郁症。该模型纳入了绝对化词语和负面情绪等认知扭曲，取得了0.94的宏观F1分数。这显著优于基线TF-IDF模型0.80的分数，证明了将认知理论整合到AI驱动的心理健康分析中的有效性。 AI

影响增强了AI在心理健康分析中的能力，有可能改善在线社区中抑郁症的早期检测。

排序理由详细介绍新颖方法和基准结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Brian Van Steen · 2026-06-02 04:00

认知语言学指标在在线社区抑郁症分析中的应用：基于DistilBERT和全息降维表示法

arXiv:2606.00026v1 Announce Type: new Abstract: This paper investigates whether combining cognitively grounded linguistic features with transformer-based embeddings improves automated detection of depression in online text. Using Beck's Cognitive Theory of Depression, the study e…