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My research was featured in a Bloomberg Businessweek article by Mark Milian on nostalgia in the 2024 presidential campaigns. The article is available here.

My research was featured in a New York Times opinion piece by Thomas Edsall on Donald Trump’s mixing of business and politics. The article is available here.

I spoke with Kai Ryssdal and Kimberly Adams about populism and the radical right in the U.S. and around the globe. Episode details are available here.

New article, co-authored with Yueran Zhang, just came out in Social Forces. Here is the abstract:

Past research has devoted more attention to the consequences of populism for party politics and democratic governance than to its effects on public attitudes—and particularly, how populist claims interact with nationalism to exacerbate exclusionary beliefs among the public. Using online survey experiments, we examine whether exposure to populism increases out-group antipathy among Democrats and Republicans. In Study 1, we randomly assign respondents to three conditions featuring vignettes based on political speeches: a morally neutral argument, a populist critique of political elites and a morally framed anti-immigration appeal. The results demonstrate that the populist treatment generates lower feeling-thermometer ratings of minority groups than the control condition, but only among Republicans and Trump supporters. Study 2 uses a similar design to evaluate the link between left-wing economic populism and economic nationalism: the populist condition features a critique of economic elites, the nationalist condition blames China for the offshoring of US jobs and the outcome variable measures respondents’ sentiments toward China. Economic nationalism generates increased anti-China sentiment among moderate and conservative Democrats but economic populism does not. Together, these findings suggest that the effects of populism on nationalist antipathies observed in Study 1 are driven by the discursive bundling of anti-elite talk with ethno-nationalism on the political right, which stands in contrast to the decoupling of economic populism from economic nationalism on the left. The former has effectively turned populism into a form of dog-whistle politics among Republicans, and Trump supporters in particular.

My research was featured in a New York Times opinion piece by Thomas Edsall on partisan polarization in the United States. The article is available here.

New article, co-authored with Oscar Stuhler, just came out in Sociological Forum. Here is the abstract:

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.

A new article, co-authored with Yuchen Luo and Oscar Stuhler, just came out in Sociological Methods & Research. Here is the abstract:

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.

A new publication, co-authored with Laura Nelson, was just published in Sociological Methods and Research. Here is the abstract:

As the field of computational text analysis within the social sciences is maturing, computational methods are no longer seen as ends in themselves, but rather as means toward answering theoretically motivated research questions. The objective of this special issue is to showcase such research: the use of novel computational methods in the service of advancing substantive scientific knowledge. In presenting the contributions to the issue, we discuss several insights that emerge from this work, which hold relevance not only for current and aspiring practitioners of computational text analysis, but also for its skeptics. These concern the central role of theory in designing and executing computational research, the selection of appropriate techniques from a rapidly growing methodological toolkit, the benefits—and risks—of methodological bricolage, and the necessity of validating all aspects of the research process. The result is a set of broad considerations concerning the effective application of computational methods to substantive questions, illustrated by eight exemplary empirical studies.

A new special issue of Sociological Methods & Research, which I co-edited with Laura Nelson, just came out. It focuses on theory-driven computational text analysis and features nine terrific methodologically innovative and substantively relevant articles. Here is the link.

My comments were featured in a New York Times opinion piece by Thomas Edsall on changes in the demographic composition of the Democratic and Republican Parties. The article is available here.