How populist are parties? : measuring degrees of populism in party manifestos using supervised machine learning
Political analysis, 2022, Vol. 30, No. 3, pp. 311-327
DI COCCO, Jessica, MONECHI, Bernardo, How populist are parties? : measuring degrees of populism in party manifestos using supervised machine learning, Political analysis, 2022, Vol. 30, No. 3, pp. 311-327 - https://hdl.handle.net/1814/72839
Retrieved from Cadmus, EUI Research Repository
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.
Published online: 15 October 2021
Cadmus permanent link: https://hdl.handle.net/1814/72839
Full-text via DOI: 10.1017/pan.2021.29
Publisher: Cambridge University Press
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