  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.
