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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.

Nostalgic appeals to an idealized past are a commonly associated with radical-right discourse. They bolster candidates’ critiques of the status quo and promises of a better future, all while mobilizing perceptions of collective status threat among supporters. In this paper, we ask whether nostalgia is a radical-right innovation or whether it has precedents in mainstream politics. We make use of recent advances in natural language processing---specifically transformer-based deep learning models---to identify nostalgic claims in U.S. presidential campaign speeches from 1952 to 2020. We then examine 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 and nationalist appeals. Our findings suggest that nostalgic rhetoric usually takes the form of brief and multivocal statements with a consistent lexical signature. It is frequently used by challenger candidates from both parties to generate a heightened sense of crisis and to morally indict incumbent opponents, particularly during times of widespread cultural contention. In so doing, nostalgia helps substantiate candidates' populist claims and expressions of low national pride. Given that these patterns are found throughout our time series, this points to important continuities between the discourse of mainstream and radical-right actors in U.S. politics. Where their respective messaging diverges, however, is in the use of nostalgia to frame exclusionary nationalist and authoritarian appeals, a practice limited to the radical-right (in our data, Donald Trump). Our findings suggest that radical-right actors did not invent their rhetorical strategies de novo, but rather, have adopted frames already widespread in mainstream politics, adapting and creatively recombining them for their own ends.

Radical-right parties and candidates combine three discursive elements in their electoral appeals: anti-elite populism, exclusionary and declinist nationalism, and illiberal authoritarianism. Recent studies have 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 paper investigates the obverse process: the radical right's (specifically, Donald Trump's) reliance on discursive elements that had long been present in mainstream institutional politics. 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 2016. These frames are subtle, infrequent, and polysemic, which makes their quantitative measurement difficult. We overcome this by leveraging the affordances of cutting-edge neural language models; in particular, we combine a variant of bidirectional encoder representations from transformers (RoBERTa) with active learning. As we demonstrate, this approach is considerably more powerful than other methods commonly used by social scientists to measure discursive frames. Our results suggest that what set Donald Trump's campaign apart from those of mainstream presidential candidates was not its invention of a new form of politics, but its 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 previously been used only in coded form. Radical-right discourse therefore appears to be less a break with the past and more an amplification and creative rearrangement of existing political-cultural tropes.

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