Social media is one of the most highly sought resources for analyzing characteristics of the language by its users. In particular, many researchers utilized various linguistic features of mental health problems from social media. However, existing approaches to detecting mental disorders face critical challenges, such as the scarcity of high-quality data or the trade-off between addressing the complexity of models and presenting interpretable results grounded in expert domain knowledge. To address these challenges, we design a simple but flexible model that preserves domain-based interpretability. We propose a novel approach that captures the semantic meanings directly from the text and compares them to symptom-related descriptions. Experimental results demonstrate that our model outperforms relevant baselines on various mental disorder detection tasks. Our detailed analysis shows that the proposed model is effective at leveraging domain knowledge, transferable to other mental disorders, and providing interpretable detection results.

翻译：社交媒体是分析用户语言特征最受追捧的资源之一。许多研究者利用社交媒体中的多种语言特征来研究心理健康问题。然而，现有的心理障碍检测方法面临关键挑战，例如高质量数据稀缺，以及模型复杂性与基于专家领域知识提供可解释结果之间的权衡。为解决这些问题，我们设计了一个简单但灵活的模型，保留了基于领域的可解释性。我们提出了一种新方法，直接从文本中捕捉语义含义，并将其与症状相关描述进行比较。实验结果表明，我们的模型在各种心理障碍检测任务中优于相关基线。我们的详细分析表明，该模型能够有效利用领域知识，可迁移至其他心理障碍，并提供可解释的检测结果。