用社交媒体文本检测注意力缺陷，兼顾可解释性与准确率
Transparent but Powerful: Explainability, Accuracy, and Generalizability in ADHD Detection from Social Media Data
- 结合浅层与深层模型分析社交文本中的语言特征
- BiLSTM在准确率与可解释性间取得较好平衡
- 跨平台验证发现关键语言标志，适合心理筛查工具开发
注意缺陷多动障碍（ADHD）是一种常见精神健康问题，影响儿童和成人，但严重低估诊。近年来，人工智能特别是自然语言处理与机器学习的发展，为利用社交媒体数据实现可扩展、非侵入式筛查提供了新路径。本文系统研究了基于社交媒体文本的ADHD检测，采用浅层机器学习与深度学习方法，包括BiLSTM和基于Transformer的模型，分析相关文本的语言模式。结果揭示不同模型在可解释性与性能间的权衡，其中BiLSTM在透明性与准确性之间取得良好平衡。此外，通过在Reddit和Twitter跨平台数据上评估模型泛化能力，识别出与ADHD相关的语言特征，有助于构建更有效的数字筛查工具。
原文摘要 · Abstract (English)
Attention-deficit/hyperactivity disorder (ADHD) is a prevalent mental health condition affecting both children and adults, yet it remains severely underdiagnosed. Recent advances in artificial intelligence, particularly in Natural Language Processing (NLP) and Machine Learning (ML), offer promising solutions for scalable and non-invasive ADHD screening methods using social media data. This paper presents a comprehensive study on ADHD detection, leveraging both shallow machine learning models and deep learning approaches, including BiLSTM and transformer-based models, to analyze linguistic patterns in ADHD-related social media text. Our results highlight the trade-offs between interpretability and performance across different models, with BiLSTM offering a balance of transparency and accuracy. Additionally, we assess the generalizability of these models using cross-platform data from Reddit and Twitter, uncovering key linguistic features associated with ADHD that could contribute to more effective digital screening tools.
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