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

Abstract

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.

Rethinking organizational ecology in light of developments in cognitive science and natural language processing

“…I believe that this paper's lessons for theory building and measurement in the context of organizational ecology have broad implications for other work in sociology that seeks to build connections to cognition. This includes such diverse fields such as cultural sociology (DiMaggio, 1997;Mohr, Bail, Frye, Lena, Lizardo, McDonnell, Mische, Tavory, and Wherry, 2020;Vaisey, 2021;Cerulo, Leschziner, and Sheperd, 2021), economic sociology (Vila-Henninger, 2021), political sociology (Bonikowski, Luo, and Sthuler, 2022), and stratification (Kozlowski et al, 2019). Pursuing the new approach might not only improve theory and measurement in these fields, but it might also reveal deeper connections among them.…”

Who Does What to Whom? Making Text Parsers Work for Sociological Inquiry

“…To study the notions of “we” invoked by U.S. presidential candidates, I use a text corpus of campaign speeches and public statements created by Bonikowski and colleagues (2021). This data combines different sources to cover the elections from 1952 to 2020 and comprises 2,956 speeches delivered by 34 candidates.…”

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

“…Recent studies have shown populism and authoritarianism—two other central components of radical‐right appeals—to have a long history in mainstream U.S. politics (Bonikowski et al. 2022; Bonikowski and Gidron 2016; Dai and Kustov 2022; Fahey 2021). Given the ubiquity of nostalgia as a basic human sentiment, its efficacy as a political framing device, and the simple ways in which it can be evoked, its use in radical politics may have mainstream precedents as well.…”

“…Sixth, our study examines the relationship between nostalgia and other frames commonly used in radical‐right discourse, including populism, two aspects of nationalism (exclusion and low pride), and authoritarianism, as measured in a recent study using the same corpus (Bonikowski et al. 2022). This follows from our earlier argument that nostalgia may serve as a mechanism that connects these discursive elements.…”

“…Such methods are superior at scale to more traditional approaches, such as manual coding or locally trained classifiers (Bonikowski et al. 2022), and are suitable for a wide range of research questions that depend on the classification of text.…”