Reclaiming the Past to Transcend the Present: Nostalgic Appeals in U.S. Presidential Elections

Bart Bonikowski, Oscar Stuhler

    Research output: Contribution to journalArticlepeer-review


    Nostalgic appeals to an idealized past are often employed in radical-right discourse. In this study, we examine precedents for this strategy in mainstream politics. We make use of recent advances in natural language processing—specifically Transformer-based neural language models and active learning—to identify instances of nostalgia in U.S. presidential campaign speeches from 1952 to 2020. We then ask what form nostalgia takes, when it has been most salient, what aspects of the nation it has been used to glorify, and how it relates to populist, nationalist, and authoritarian frames. Our findings demonstrate that nostalgic appeals tend not to involve rich descriptions of bygone historical periods, but instead take the form of brief and multivocal statements with a consistent lexical signature. Moreover, nostalgia is frequently used by challenger candidates from both parties to reinforce populist claims and expressions of low national pride. This points to discursive continuities between mainstream and radical-right actors. Where their respective messaging diverges is in the use of nostalgia to frame exclusionary nationalist and authoritarian claims, a practice limited to radical-right campaigns (in our data, those of Donald Trump). Rather than inventing their rhetorical strategies de novo, therefore, it appears that radical-right actors tend to adopt and creatively recombine frames already widespread in political culture.

    Original languageEnglish (US)
    Pages (from-to)1263-1293
    Number of pages31
    JournalSociological Forum
    Issue numberS1
    StatePublished - Dec 2022


    • authoritarianism
    • computational text analysis
    • elections
    • nationalism
    • nostalgia
    • political discourse
    • politics
    • populism
    • radical right

    ASJC Scopus subject areas

    • Sociology and Political Science


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