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dc.contributor.authorFrischlich, Lena-
dc.contributor.authorClever, Lena-
dc.contributor.authorWulf, Tim-
dc.contributor.authorWildschut, Tim-
dc.contributor.authorSedikides, Constantine-
dc.date.accessioned2024-06-07T16:11:58Z-
dc.date.available2024-06-07T16:11:58Z-
dc.date.issued2023-
dc.identifier.citationFrischlich, L., Clever, L., Wulf, T., Wildschut, T., and Sedikides, C. (2023). Digital Memory and Populism| Populists’ Use of Nostalgia: A Supervised Machine Learning Approach. International Journal Of Communication, 17, 25. https://ijoc.org/index.php/ijoc/article/view/19063/4102es_ES
dc.identifier.issn1932-8036-
dc.identifier.urihttps://repositorio.consejodecomunicacion.gob.ec//handle/CONSEJO_REP/7446-
dc.description.abstractAn emotion that has recently gained traction in the context of populism is nostalgia, a sentimental longing or wistful affection for the past. Nostalgia can refer to the past of one’s group or nation, as reflected in populists’ narratives of the heartland—the vision of a utopian future based on an idealized past in which their country belonged to the “pure people.” However, research on nostalgia in political communication across the political aisle is scarce. The current study aimed to fill this gap via supervised machine learning. First, we used an experimental approach established in psychology to create a ground-truth data set and trained and evaluated a classifier for detecting nostalgic sentiment in the German language. We then applied this classifier to a large database (N = 4,022) of German political parties’ Facebook posts. We demonstrate that (a) populist (vs. non-populist)—especially right-wing—parties employ nostalgia more frequently; (b) nostalgic narratives differ between parties, and (c) nostalgic (vs. non-nostalgic) posts are associated with more user engagement.es_ES
dc.language.isoenes_ES
dc.publisherInternational Journal of Communicationes_ES
dc.subjectpopulismes_ES
dc.subjectpoliticales_ES
dc.subjectnostalgiaes_ES
dc.titleDigital Memory and Populism| Populists’ Use of Nostalgia: A Supervised Machine Learning Approaches_ES
dc.title.alternativeInternational Journal of Communicationes_ES
dc.typeArticlees_ES
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