用注意力机制提升社交媒体抑郁检测精度，降低标注成本。
LMILAtt: A Deep Learning Model for Depression Detection from Social Media Users Enhanced by Multi-Instance Learning Based on Attention Mechanism
- 结合LSTM自编码器与注意力机制，捕捉用户发帖的动态变化模式。
- 在WU3D数据集上，准确率、召回率和F1值均显著优于基线模型。
- 弱监督学习减少人工标注，适合大规模社交平台抑郁筛查。
抑郁症是全球重大公共卫生挑战，早期识别至关重要。社交媒体数据为抑郁检测提供了新视角，但现有方法存在准确性不足、时间序列特征利用不充分及标注成本高等问题。为此，本文提出LMILAtt模型，创新性地融合长短期记忆自编码器与注意力机制：首先通过无监督LSTM自编码器提取用户推文的时间动态特征（如抑郁倾向演化模式）；其次利用注意力机制动态加权关键文本（如早期抑郁信号），构建多实例学习架构以提升用户级检测准确率。最终在专业医疗人员标注的WU3D数据集上验证性能。实验表明，该模型在准确率、召回率和F1分数上均显著优于基线模型。此外，弱监督学习策略大幅降低标注成本，为大规模社交媒体抑郁筛查提供高效解决方案。
原文摘要 · Abstract (English)
Depression is a major global public health challenge and its early identification is crucial. Social media data provides a new perspective for depression detection, but existing methods face limitations such as insufficient accuracy, insufficient utilization of time series features, and high annotation costs. To this end, this study proposes the LMILAtt model, which innovatively integrates Long Short-Term Memory autoencoders and attention mechanisms: firstly, the temporal dynamic features of user tweets (such as depressive tendency evolution patterns) are extracted through unsupervised LSTM autoencoders. Secondly, the attention mechanism is used to dynamically weight key texts (such as early depression signals) and construct a multi-example learning architecture to improve the accuracy of user-level detection. Finally, the performance was verified on the WU3D dataset labeled by professional medicine. Experiments show that the model is significantly better than the baseline model in terms of accuracy, recall and F1 score. In addition, the weakly supervised learning strategy significantly reduces the cost of labeling and provides an efficient solution for large-scale social media depression screening.
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