Previous studies suggested that the 2016 presidential elections gave rise to pathological levels of election-related distress in liberal Americans; however, it has also been suggested that the public discourse and the professional discourse have increasingly overgeneralized concepts of trauma and psychopathology. In light of this, in the current research, we utilized an array of big data measures and asked whether a political loss in a participatory democracy can indeed lead to psychopathology. We observed that liberals report being more depressed when asked directly about the effects of the election; however, more indirect measures show a short-lived or nonexistent effect. We examined self-report measures of clinical depression with and without a reference to the election (Studies 1A & 1B), analyzed Twitter discourse and measured users’ levels of depression using a machine-learning-based model (Study 2), conducted time-series analysis of depression-related search behavior on Google (Study 3), examined the proportion of antidepressants consumption in Medicaid data (Study 4), and analyzed daily surveys of hundreds of thousands of Americans (Study 5), and saw that at the aggregate level, empirical data reject the accounts of “Trump Depression.” We discuss possible interpretations of the discrepancies between the direct and indirect measures. The current investigation demonstrates how big-data sources can provide an unprecedented view of the psychological consequences of political events and sheds light on the complex relationship between the political and the personal spheres.
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© 2020 American Psychological Association
- big data
- social cognition
- social media