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Individual differences in psychological reactivities (i.e., the degree to which individuals react differently to social interactions) are central to psychological research. Previous theory-based research has identified substantial individual differences in reactivities but few robust predictors of these differences. This work aimed to address two questions: First, can individual differences in reactivities to social interactions be accurately predicted at all? Second, what are the most important person-level variables for this prediction? A data-driven machine learning approach was applied to three large-scale experience sampling data sets (overall N = 5,047) to predict the extent to which individuals reacted with positive and negative affect to momentary social interaction characteristics (e.g., interaction depth). Individual differences in reactivities were extracted via multilevel modeling (i.e., random slopes) and then predicted with machine learning methods using a variety of person-level variables (i.e., sociodemographics, personality traits, and political and societal attitudes). The robustness of predictions was examined by built-in cross-validation and across independent samples. Feature importance and interactions were analyzed with SHapley Additive exPlanations values. Our results suggest that, whereas complex prediction models outperformed a baseline model in predicting individual differences in reactivities in most analyses, the overall predictive performance was limited. This finding underlines the importance of replicating machine learning results across outcomes and independent samples. We revealed several predictive patterns that can stimulate future research, elaborate on limitations of current machine learning approaches for intensive within-person data, and discuss the results against the background of dynamic conceptualizations of personality. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
