用社交媒体文本和时间特征，提前识别抑郁焦虑等心理危机。
Early Detection of Mental Health Issues Using Social Media Posts
- 结合文本与时间序列的双流BiLSTM模型，捕捉情绪变化模式。
- 在Reddit数据集上达74.55%准确率和0.7376的F1分数。
- 适合心理健康预警系统研发者参考，可扩展多模态数据。
抑郁症、焦虑症和双相情感障碍等心理健康问题日益普遍，亟需早期检测与干预工具。社交平台如Reddit提供了丰富的用户生成内容，反映了情绪与行为模式。本文提出一种多模态深度学习框架，融合语言特征与时间特征，用于早期心理危机检测。该方法采用双向LSTM（BiLSTM）分别处理文本与时间序列数据，以建模其序列依赖性，并通过跨模态注意力机制融合输出，实现上下文感知的分类。模型在经文本预处理、时间特征归一化及标签编码后的标注Reddit数据集上训练与评估。实验结果表明，所提架构优于传统模型，验证集准确率达74.55%，F1得分0.7376。本研究强调多模态学习在心理健康检测中的价值，并为后续引入更先进注意力机制及其他数据模态提供基准。
原文摘要 · Abstract (English)
The increasing prevalence of mental health disorders, such as depression, anxiety, and bipolar disorder, calls for immediate need in developing tools for early detection and intervention. Social media platforms, like Reddit, represent a rich source of user-generated content, reflecting emotional and behavioral patterns. In this work, we propose a multi-modal deep learning framework that integrates linguistic and temporal features for early detection of mental health crises. Our approach is based on the method that utilizes a BiLSTM network both for text and temporal feature analysis, modeling sequential dependencies in a different manner, capturing contextual patterns quite well. This work includes a cross-modal attention approach that allows fusion of such outputs into context-aware classification of mental health conditions. The model was then trained and evaluated on a dataset of labeled Reddit posts preprocessed using text preprocessing, scaling of temporal features, and encoding of labels. Experimental results indicate that the proposed architecture performs better compared to traditional models with a validation accuracy of 74.55% and F1-Score of 0.7376. This study presents the importance of multi-modal learning for mental health detection and provides a baseline for further improvements by using more advanced attention mechanisms and other data modalities.
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