Individuals high in social anxiety symptoms often exhibit elevated state anxiety in social situations. Research has shown it is possible to detect state anxiety by leveraging digital biomarkers and machine learning techniques. However, most existing work trains models on an entire group of participants, failing to capture individual differences in their psychological and behavioral responses to social contexts. To address this concern, in Study 1, we collected linguistic data from N=35 high socially anxious participants in a variety of social contexts, finding that digital linguistic biomarkers significantly differ between evaluative vs. non-evaluative social contexts and between individuals having different trait psychological symptoms, suggesting the likely importance of personalized approaches to detect state anxiety. In Study 2, we used the same data and results from Study 1 to model a multilayer personalized machine learning pipeline to detect state anxiety that considers contextual and individual differences. This personalized model outperformed the baseline F1-score by 28.0%. Results suggest that state anxiety can be more accurately detected with personalized machine learning approaches, and that linguistic biomarkers hold promise for identifying periods of state anxiety in an unobtrusive way.

翻译：高社交焦虑症状个体在社交情境中常表现出升高的状态焦虑。研究表明，利用数字生物标志物和机器学习技术可以检测状态焦虑。然而，现有研究大多基于整组参与者训练模型，未能捕捉个体对社会情境心理和行为反应的差异。为解决这一问题，研究1收集了35名高社交焦虑参与者在多种社交情境中的语言数据，发现数字语言生物标志物在评价性vs非评价性社交情境之间以及具有不同特质心理症状的个体之间存在显著差异，表明个性化方法对检测状态焦虑可能具有重要意义。在研究2中，我们采用研究1的相同数据和结果，构建了一个考虑情境和个体差异的多层个性化机器学习管道来检测状态焦虑。该个性化模型的F1得分较基线提高了28.0%。结果表明，采用个性化机器学习方法可以更准确地检测状态焦虑，且语言生物标志物有望以无干扰方式识别状态焦虑期。