How populist are parties? : measuring degrees of populism in party manifestos using supervised machine learning

Abstract

One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here, we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information for temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the party landscape while reducing the risk of arbitrary classifications. To illustrate the potential contribution of this score, we use it as a proxy for parties’ levels of populism, analyzing average trends in six European countries from the early 2000s for nearly two decades.