Stereotypes of the two parties play an important role in political cognition, and a range of recent studies have examined the content and effects of partisan stereotypes. However, little work has studied change in partisan stereotypes over time. We address this question by comparing data on stereotypes of partisans collected before and after the Trump presidency, a time when we might expect individuals' images of the two parties to undergo significant change. Using a structural topic model, we compare responses to open-ended questions asking respondents to list words describing members of the two parties from 2016 and 2021. We find that partisan stereotypes in the 2021 sample are less group- and issue-based and focused more on personal traits. These results suggest that, during the Trump era, members of the mass public came to see the parties in more personalized, character-focused terms, potentially contributing to affective polarization.

We propose and explore the possibility that language models can be studied as effective proxies for specific human subpopulations in social science research. Practical and research applications of artificial intelligence tools have sometimes been limited by problematic biases (such as racism or sexism), which are often treated as uniform properties of the models. We show that the “algorithmic bias” within one such tool—the GPT-3 language model—is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups. We term this property algorithmic fidelity and explore its extent in GPT-3. We create “silicon samples” by conditioning the model on thousands of sociodemographic backstories from real human participants in multiple large surveys conducted in the United States. We then compare the silicon and human samples to demonstrate that the information contained in GPT-3 goes far beyond surface similarity. It is nuanced, multifaceted, and reflects the complex interplay between ideas, attitudes, and sociocultural context that characterize human attitudes. We suggest that language models with sufficient algorithmic fidelity thus constitute a novel and powerful tool to advance understanding of humans and society across a variety of disciplines.

The last sentence of the abstract for “Football and Public Opinion: A Partial Replication and Extension” as published online in the Journal of Experimental Political Science on 8 November, 2017 contained the following error:

While the effects clearly can occur, there relevance to politics remains unclear.

It should have read as:

While the effects clearly can occur, their relevance to politics remains unclear.

The author and publisher apologize for this error.

Do events irrelevant to politics, such as the weather and sporting events, affect political opinions? A growing experimental literature suggests that such events can matter. However, extant experimental evidence may over-state irrelevant event effects; this could occur if these studies happen to focus on particular scenarios where irrelevant event effects are likely to occur. One way to address this possibility is through replication, which is what we do. Specifically, we replicate an experimental study that showed the outcome of a college football game can influence presidential approval. Our results partially replicate the previous study and suggest the impact is constrained to a limited set of outcome variables. The findings accentuate the need for scholars to identify the conditions under which irrelevant effects occur. While the effects clearly can occur, there relevance to politics remains unclear.