A Text Mining Approach to Measure Consistency in Self-Reported Situational Causes for Irritability (Preprint)

BACKGROUND Irritability, a transdiagnostic symptom linked to severe functional impairment and suicide risk, comprises of tonic irritability (i.e., chronic irritable mood) and phasic irritability (i.e., episodic anger outbursts). However, the consistency in situational triggers for irritability (i.e., irritability-related stressors) has not been thoroughly studied. OBJECTIVE This study uses text mining to create a metric for consistency in irritability-related stressors and examines its association with daily irritability. METHODS Ninety-seven participants (47% female; age: M = 38.85, SD = 10.62; 16% ethno-racial minority) with self-reported depression completed a baseline survey and up to 18 days of daily diaries. We computed the semantic similarity between daily text descriptions of irritability-related stressors to estimate within-person consistency across days. We applied linear regression, mixed-effects linear model, and permutation regression to test the metric’s fairness, relations to daily tonic and phasic irritability separately, and associations with the intraindividual covariance between tonic and phasic irritability. RESULTS The constructed metric showed no demographic differences. The metric was negatively associated with baseline phasic irritability (β = 0.23, p