Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in U.S. Presidential Campaigns (1952–2020) with Neural Language Models

Bart Bonikowski, Yuchen Luo, Oscar Stuhler

    Research output: Contribution to journalArticlepeer-review


    Radical-right campaigns commonly employ three discursive elements: anti-elite populism, exclusionary and declinist nationalism, and authoritarianism. Recent scholarship has explored whether these frames have diffused from radical-right to centrist parties in the latter’s effort to compete for the former’s voters. This study instead investigates whether similar frames had been used by mainstream political actors prior to their exploitation by the radical right (in the U.S., Donald Trump’s 2016 and 2020 campaigns). To do so, we identify instances of populism, nationalism (i.e., exclusionary and inclusive definitions of national symbolic boundaries and displays of low and high national pride), and authoritarianism in the speeches of Democratic and Republican presidential nominees between 1952 and 2020. These frames are subtle, infrequent, and polysemic, which makes their measurement difficult. We overcome this by leveraging the affordances of neural language models—in particular, a robustly optimized variant of bidirectional encoder representations from Transformers (RoBERTa) and active learning. As we demonstrate, this approach is more effective for measuring discursive frames than other methods commonly used by social scientists. Our results suggest that what set Donald Trump’s campaign apart from those of mainstream presidential candidates was not the invention of a new form of politics, but the combination of negative evaluations of elites, low national pride, and authoritarianism—all of which had long been present among both parties—with an explicit evocation of exclusionary nationalism, which had been articulated only implicitly by prior presidential nominees. Radical-right discourse—at least at the presidential level in the United States—should therefore be characterized not as a break with the past but as an amplification and creative rearrangement of existing political-cultural tropes.

    Original languageEnglish (US)
    Pages (from-to)1721-1787
    Number of pages67
    JournalSociological Methods and Research
    Issue number4
    StatePublished - Nov 2022


    • BERT
    • Transformer models
    • active learning
    • computational text analysis
    • cultural frames
    • machine learning
    • nationalism
    • natural language processing
    • populism
    • radical politics

    ASJC Scopus subject areas

    • Social Sciences (miscellaneous)
    • Sociology and Political Science


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