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Sensor data collected from smartphones provides the possibility to passivelyinfer a user's personality traits. Such models can be used to enable technologypersonalization, while contributing to our substantive understanding of howhuman behavior manifests in daily life. A significant challenge in personalitymodeling involves improving the accuracy of personality inferences, however,research has yet to assess and consider the cultural impact of users' countryof residence on model replicability. We collected mobile sensing data andself-reported Big Five traits from 166 participants (54 women and 112 men)recruited in five different countries (UK, Spain, Colombia, Peru, and Chile)for 3 weeks. We developed machine learning based personality models usingculturally diverse datasets -- representing different countries -- and we showthat such models can achieve state-of-the-art accuracy when tested in newcountries, ranging from 63% (Agreeableness) to 71% (Extraversion) ofclassification accuracy. Our results indicate that using country-specificdatasets can improve the classification accuracy between 3% and 7% forExtraversion, Agreeableness, and Conscientiousness. We show that these findingshold regardless of gender and age balance in the dataset. Interestingly, usinggender- or age- balanced datasets as well as gender-separated datasets improvetrait prediction by up to 17%. We unpack differences in personality modelsacross the five countries, highlight the most predictive data categories(location, noise, unlocks, accelerometer), and provide takeaways totechnologists and social scientists interested in passive personalityassessment.

Modeling Personality vs. Modeling Personalidad: In-the-wild Mobile Data Analysis in Five Countries Suggests Cultural Impact on Personality Models