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Personal electronic devices including smartphones give access to behaviouralsignals that can be used to learn about the characteristics and preferences ofindividuals. In this study, we explore the connection between demographic andpsychological attributes and the digital behavioural records, for a cohort of7,633 people, closely representative of the US population with respect togender, age, geographical distribution, education, and income. Along with thedemographic data, we collected self-reported assessments on validatedpsychometric questionnaires for moral traits and basic human values andcombined this information with passively collected multi-modal digital datafrom web browsing behaviour and smartphone usage. A machine learning frameworkwas then designed to infer both the demographic and psychological attributesfrom the behavioural data. In a cross-validated setting, our models predicteddemographic attributes with good accuracy as measured by the weighted AUROCscore (Area Under the Receiver Operating Characteristic), but were lessperformant for the moral traits and human values. These results call forfurther investigation since they are still far from unveiling individuals'psychological fabric. This connection, along with the most predictive featuresthat we provide for each attribute, might prove useful for designingpersonalised services, communication strategies, and interventions, and can beused to sketch a portrait of people with a similar worldview.

Predicting Demographics, Moral Foundations, and Human Values from Digital Behaviors