When Do Politicians Use Populist Rhetoric? Populism as a Campaign Gamble

Dai, Yaoyao; Kustov, Alexander

doi:10.1080/10584609.2022.2025505

Cited by 21 publications

(25 citation statements)

References 58 publications

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“…In this sense, mainstream parties’ deployment of faux-radicalism is a high-risk gamble (cf. Dai and Kustov 2022): it may be electorally effective in the short run, but in the long run, it can undermine those parties’ positions by facilitating successful challenges from the periphery.…”

Section: The Radical Right Versus the Mainstreammentioning

confidence: 99%

“…Although populism is one of the central features of radical-right politics, it is not inherently tied to any particular political ideology (Laclau 2005). Indeed, it has been a common trope among politicians of the radical left as well—in Latin America (Levitsky and Loxton 2013; Roberts 1995; Weyland 2001), Western Europe (March 2012), and the United States (Goodwyn 1978; Kazin 1998)—and also among center-left and center-right parties, such as the U.S. Democrats and Republicans (Bonikowski and Gidron 2016; Fahey 2021; Dai and Kustov 2022).…”

Section: Defining Populism Nationalism and Authoritarianismmentioning

confidence: 99%

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

Bonikowski

Luo²,

Stuhler³

2022

Sociological Methods & Research

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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.

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Section: The Radical Right Versus the Mainstreammentioning

confidence: 99%

Section: Defining Populism Nationalism and Authoritarianismmentioning

confidence: 99%

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

Bonikowski

Luo²,

Stuhler³

2022

Sociological Methods & Research

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“…Many of the limitations identified in this paper can be traced back to DCM's decision not to code populism at the sentence or paragraph level. Recently, for example, Dai and Kustov (2022) have shown that supervised machine-learning models can identify populism in texts. They code paragraphs as populist or non-populist and then train machine-learning models using word embeddings.…”

Section: Discussionmentioning

confidence: 99%

When Correlation is Not Enough: Validating Populism Scores from Supervised Machine-Learning Models

Jankowski¹,

Huber²

2022

Preprint

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Despite the on-going success of populist parties in many parts of the world, we lack comprehensive information about parties' level of populism over time. A recent contribution to Political Analysis by Di Cocco and Monechi (DCM) suggests that this research gap can be closed by predicting parties' populism scores from their election manifestos using supervised machine-learning. In this paper, we provide a detailed discussion of the suggested approach. Building on recent debates about the validation of machine-learning models, we argue that the validity checks provided in DCM's paper are insufficient. We conduct a series of additional validity checks and empirically demonstrate that the approach is not suitable for deriving populism scores from texts. We conclude that measuring populism over time and between countries remains an immense challenge for empirical research. More generally, our paper illustrates the importance of more comprehensive validations of supervised machine-learning models.

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“…Challengers, in contrast, only stand to gain from criticizing the state of the country, because by doing so, they also undermine their incumbent opponents (Crabtree et al, 2020). This is a dynamic that has been documented for populist claims (Bonikowski and Gidron, 2016;Dai and Kustov, 2021;Fahey, 2021) and it may be relevant for nostalgia as well, since both frames paint the present in a negative light. The transition between Donald Trump's 2016 slogan "Make America Great Again" and its non-nostalgic, and arguably less compelling, 2020 variant "Keep America Great" is case in point.…”

Section: Theoretical Expectationsmentioning

confidence: 99%

“…To improve these predictions, we build on active learning, another innovation in machine learning that has recently gained popularity (Dor et al 2020; for applications, see Bonikowski et al 2021;Dai and Kustov 2020). Active learning involves an iterative process, whereby the machine learning classifier identifies ambiguous texts, which are then hand-coded by the researcher and returned to the training set, thereby (ideally) improving the model's accuracy in a subsequent round of training and classification.…”

Section: Measuring Nostalgia With Transformers and Active Learningmentioning

confidence: 99%

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

Bonikowski

Stuhler

2022

Sociological Forum

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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 radicalright 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.

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When Do Politicians Use Populist Rhetoric? Populism as a Campaign Gamble

Cited by 21 publications

References 58 publications

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

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

When Correlation is Not Enough: Validating Populism Scores from Supervised Machine-Learning Models

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

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