构建心理状态与关系互动关联的基准数据集，助力情境化心理健康分析
RSPC: A Benchmark for Modeling Stress and Psychiatric Conditions in Digitally Mediated Relationships using Psychiatrist Annotations
- 基于红迪网长期异地恋帖子，结合精神科医生标注构建多标签数据集
- 模型表现因任务而异：Claude-3-Haiku在情绪障碍分类中最佳（F1=0.538）
- 发现焦虑与长期关系不确定性显著相关，支持关系情境建模的重要性
在自然语言处理领域，心理健康常被视作孤立现象，缺乏人际背景。本文利用红迪网关于异地恋的帖子，捕捉心理困扰及其关联的关系触发因素。我们提出了关系压力与精神疾病语料库（RSPC），包含1,799篇经精神科医生标注的帖子，涵盖最常见的情绪障碍（焦虑、抑郁）、关系压力源及关系阶段。我们在多标签疾病分类、关系触发词检测和时间阶段预测任务上，对七种微调的Transformer模型和五种大语言模型进行基准测试。结果表明模型性能具有任务依赖性：Claude-3-Haiku在疾病分类中表现最佳（宏平均F1=0.538），GPT-4o在触发词检测中最强（宏平均F1=0.519），揭示不同模型的能力差异。研究还发现焦虑障碍与持续的关系不确定性存在强关联。RSPC为考虑关系上下文的NLP任务建立了基准，推动心理健康建模从个体中心转向情境感知，捕捉压力的社会与时间动态。
原文摘要 · Abstract (English)
In NLP, mental health conditions are often modeled as isolated phenomena, without interpersonal context. We use Reddit posts about long-distance relationships to capture both mental health distress and associated relational triggers. We introduce the Relational Stress and Psychiatry Corpus (RSPC) containing 1,799 Reddit posts annotated by psychiatrists for diagnostic categories, including the most prevalent mood disorders (anxiety and depression), relational stressor triggers, and indications of relationship phase. We benchmark seven fine-tuned transformer models and five large language models across multi-label disorder classification, relational trigger detection, and temporal phase prediction tasks. We find clear task-dependent differences between model families, with Claude-3-Haiku achieving the best disorder classification performance (Macro-F1 = 0.538) and GPT-4o obtaining the strongest relational trigger detection performance (Macro-F1 = 0.519), suggesting distinct model capabilities. We further find strong associations between anxiety disorders and chronic relational uncertainty. Overall, RSPC establishes a benchmark for NLP tasks that consider relational context and supports a shift from individual-centric to context-aware mental health modeling that captures the social and temporal dynamics of distress.
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