The Digital Authoritarian: Theory-Driven Predictions from Everyday Behaviors Collected with Smartphones

Right-wing Authoritarianism (RWA) is on the rise globally. As a result, researchers are trying to understand how this defining aspect of 20th century history is manifesting in the 21st century digital era. Individual differences in RWA have been studied extensively from a theoretical standpoint. In addition, a great deal of empirical research has examined the situational antecedents (e.g., threat) and attitudinal consequences (e.g., prejudice) of authoritarianism. However, this theoretical and empirical work has failed to paint a holistic picture of authoritarians’ behaviors in daily life. Here, digital traces from smartphone use, which have informed the study of individual differences in other domains, represent a promising means to investigate the new authoritarians in their daily lives. Given the importance of understanding this phenomenon in the current geopolitical context, our study will create a theoretically-informed profile of everyday behaviors related to authoritarianism in the digital era. To this end, we drew on the literature to derive a comprehensive overview of empirical reports on behavioral indicators of authoritarianism. We then translated these findings into behavioral features (organized into five interrelated theoretical attributes) which can be captured using data collected from smartphone sensors and logs (e.g., communication, app-use, mobility, music/podcast consumption). Where possible, we plan to enrich sensed behavioral data with data from other sources to ensure that our features reflect the theoretical claims about authoritarianism as closely as possible. Lastly, we will use cross-validated machine learning models (i.e., Elastic Net and Random Forest) to determine whether we can predict self-reported authoritarianism from these behavioral features, using data from a representative sample of 749 participants who were tracked continuously for up to six months. unknown other