Populists’ reliance on nostalgia: A supervised machine learning approach

Abstract

An 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 dataset and trained a classifier for detecting nostalgic sentiment in German language (with satisfactory reliability: f1 = .79). 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-populists)—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.

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Identifiers

ORCID for Tim Wildschut: orcid.org/0000-0002-6499-5487

ORCID for Constantine Sedikides: orcid.org/0000-0003-4036-889X

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