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Digital Memory and Populism| Populists’ Use of Nostalgia: A Supervised Machine Learning Approach


 
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1. Title Title of document Digital Memory and Populism| Populists’ Use of Nostalgia: A Supervised Machine Learning Approach
 
2. Creator Author's name, affiliation, country Lena Frischlich; Department of Communication, University of Münster; Germany
 
2. Creator Author's name, affiliation, country Lena Clever; Department of Information Systems, University of Münster; Germany
 
2. Creator Author's name, affiliation, country Tim Wulf; Department of Media and Communication, Ludwig-Maximilians-University Munich; Germany
 
2. Creator Author's name, affiliation, country Tim Wildschut; Center for Research on Self and Identity, School of Psychology, Unversity of Southampton, UK; United Kingdom
 
2. Creator Author's name, affiliation, country Constantine Sedikides; Center for Research on Self and Identity, School of Psychology, University of Southampton; United Kingdom
 
3. Subject Discipline(s)
 
3. Subject Keyword(s) automated text analysis, classifier development, German, Facebook, nostalgia, populism, political communication, supervised machine learning
 
4. Description 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 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.

 
5. Publisher Organizing agency, location USC Annenberg School for Communication & Journalism
 
6. Contributor Sponsor(s) The first two authors were supported by the Digital Society research program funded by the North-Rhine Westphalian Ministry of Culture and Science; Data was partially provided via CrowdTangle a Meta-Company
 
7. Date (YYYY-MM-DD) 2023-03-02
 
8. Type Status & genre Peer-reviewed Article
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Uniform Resource Identifier https://ijoc.org/index.php/ijoc/article/view/19063
 
11. Source Title; vol., no. (year) International Journal of Communication; Vol 17 (2023)
 
12. Language English=en en
 
13. Relation Supp. Files Supplementary Material for Populists’ Use of Nostalgia: A Supervised Machine Learning Approach (41KB)
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
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