How populist are parties? : measuring degrees of populism in party manifestos using supervised machine learning
| dc.contributor.author | DI COCCO, Jessica | |
| dc.contributor.author | MONECHI, Bernardo | |
| dc.date.accessioned | 2021-10-25T12:47:18Z | |
| dc.date.available | 2021-10-25T12:47:18Z | |
| dc.date.issued | 2022 | |
| dc.description | Published online: 15 October 2021 | |
| dc.description.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. | en |
| dc.identifier.citation | Political analysis, 2022, Vol. 30, No. 3, pp. 311-327 | en |
| dc.identifier.doi | 10.1017/pan.2021.29 | |
| dc.identifier.endpage | 327 | |
| dc.identifier.issue | 3 | |
| dc.identifier.startpage | 311 | |
| dc.identifier.uri | https://hdl.handle.net/1814/72839 | |
| dc.identifier.volume | 30 | |
| dc.language.iso | en | en |
| dc.orcid.upload | true | * |
| dc.publisher | Cambridge University Press | en |
| dc.relation.ispartof | Political analysis | en |
| dc.rights | info:eu-repo/semantics/openAccess | en |
| dc.rights.license | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Populism | en |
| dc.subject | Textual analysis | en |
| dc.subject | Text-as-data | en |
| dc.subject | Political parties | en |
| dc.subject | Computational politics | en |
| dc.title | How populist are parties? : measuring degrees of populism in party manifestos using supervised machine learning | en |
| dc.type | Article | en |
| dspace.entity.type | Publication | |
| eui.subscribe.skip | true | |
| person.identifier.orcid | 0000-0001-8355-6730 | |
| person.identifier.other | 46510 | |
| relation.isAuthorOfPublication | 6d2aa228-6f99-44cf-ad12-4b46fae14810 | |
| relation.isAuthorOfPublication.latestForDiscovery | 6d2aa228-6f99-44cf-ad12-4b46fae14810 |
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