Using "Big Data" Versus Alternative Measures of Aggregate Data to Predict the US 2016 Presidential Election

To what extent could "Big Data" predict the results of the 2016 U.S. presidential election better than more conventional sources of aggregate measures? To test this idea, the present research used Google search trends versus other forms of state-level data (i.e., both behavioral measures like the incidence of hate crimes, hate groups, and police brutality and implicit measures like Implicit Association Test (IAT) data) to predict each state's popular vote for the 2016 presidential election. Results demonstrate that, when taken in isolation, zero-order correlations reveal that prevalence of hate groups, prevalence of hate crimes, Google searches for racially charged terms (i.e., related to White supremacy groups, racial slurs, and the Nazi movement), and political conservatism were all significant predictors of popular support for Trump. However, subsequent hierarchical regression analyses show that when these predictors are considered simultaneously, only Google search data for historical White supremacy terms (e.g., "Adolf Hitler") uniquely predicted election outcomes earlier and beyond political conservatism. Thus, Big Data, in the form of Google search, emerged as a more potent predictor of political behavior than other aggregate measures, including implicit attitudes and behavioral measures of racial bias. Implications for the role of racial bias in the 2016 presidential election in particular and the utility of Google search data more generally are discussed.