论文标题
间接识别自然语言的社会心理风险
Indirect Identification of Psychosocial Risks from Natural Language
论文作者
论文摘要
在围产期期间，包括抑郁症和亲密伴侣暴力在内的社会心理健康风险与父母和孩子的严重不良健康结果有关。要适当干预，医疗保健专业人员必须首先确定有风险的人，但是污名通常会阻止人们直接披露提示评估所需的信息。我们研究了引起和分析可能表明社会心理风险的信息的间接方法。 Peripartum妇女的简短日记条目表现出主题建模提取的主题模式，并从情感的角度提取，是从字典信息的情感特征中汲取的。使用这些功能，我们使用正则回归来预测亲密伴侣的抑郁症和心理侵略的筛查措施。通过主题模型和情感特征量化的期刊文本条目显示出对抑郁预测的希望，其性能几乎与封闭形式的问题一样好。基于文本的功能对于预测亲密伴侣暴力的有用不太有用，但是允许在没有明确披露的情况下进行检测中等间接的多项选择询问。两种方法都可以用作检测污名风险的初始或互补筛查方法。
During the perinatal period, psychosocial health risks, including depression and intimate partner violence, are associated with serious adverse health outcomes for parents and children. To appropriately intervene, healthcare professionals must first identify those at risk, yet stigma often prevents people from directly disclosing the information needed to prompt an assessment. We examine indirect methods of eliciting and analyzing information that could indicate psychosocial risks. Short diary entries by peripartum women exhibit thematic patterns, extracted by topic modeling, and emotional perspective, drawn from dictionary-informed sentiment features. Using these features, we use regularized regression to predict screening measures of depression and psychological aggression by an intimate partner. Journal text entries quantified through topic models and sentiment features show promise for depression prediction, with performance almost as good as closed-form questions. Text-based features were less useful for prediction of intimate partner violence, but moderately indirect multiple-choice questioning allowed for detection without explicit disclosure. Both methods may serve as an initial or complementary screening approach to detecting stigmatized risks.
