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Both in their quantity and their quality, informal political conversations can provide an important bellwether for democratic health. However, not everyone is willing to participate in political conversations in all settings, and systematic imbalances in who chooses not to share political attitudes can distort perceptions of public opinion. Using data from three original surveys, including both observational and experimental analysis, we examine people's decisions to initiate political discussions using a psychological framework of self-threat and self-affirmation. We find that political conversations pose a higher level of self-threat when disagreement is probable and the relationship with the potential discussion partner is weaker. High levels of self-threat, measured via self-reported anxiety, are associated with a lower willingness to initiate a political conversation. However, self-threat can be counteracted. While it does not reduce the anxiety associated with a threatening situation, self-affirmation increases people's willingness to initiate a political conversation in higher threat circumstances. This suggests that efforts to find common ground or boost confidence by reflecting on non-political values could increase the pool of people willing to bring up and share their political views. [ABSTRACT FROM AUTHOR]

"Cancel culture" has become a prominent phrase in US political commentary, with supporters and opponents relying on different value-based arguments to express their views. At the same time, these camps often fall along predictable partisan political lines. What, then, are the real motivations for promoting and opposing behaviors sometimes labeled as "canceling" in contemporary American politics? We explore this question through survey data from Pew in 2020 and two original survey experiments, conducted on the 2021 CES and a 2023 online sample. We examine how Americans define the term "cancel culture," to what extent cancel culture is linked to both a range of core democratic values, and the role of partisanship in shaping support or opposition to specific behaviors. We observe a significant range of views about cancel culture and document connections to various political values. From our experiments, we find that partisanship is a potent driver of support for and understanding of cancel culture and that value-based framing has a weaker impact as compared to partisanship. Cancel culture provides an important case study of how Americans process conflicting norms and values, including free speech and political accountability, beyond the typical constraints of the formal political system. [ABSTRACT FROM AUTHOR]

We show that a common method of predicting individuals' race in administrative records, Bayesian Improved Surname Geocoding (BISG), produces misclassification errors that are strongly correlated with demographic and socioeconomic factors. In addition to the high error rates for some racial subgroups, the misclassification rates are correlated with the political and economic characteristics of a voter's neighborhood. Racial and ethnic minorities who live in wealthy, highly educated, and politically active areas are most likely to be misclassified as white by BISG. Inferences about the relationship between sociodemographic factors and political outcomes, like voting, are likely to be biased in models using BISG to infer race. We develop an improved method in which the BISG estimates are incorporated into a machine learning model that accounts for class imbalance and incorporates individual and neighborhood characteristics. Our model decreases the misclassification rates among non-white individuals, in some cases by as much as 50%. [ABSTRACT FROM AUTHOR]

Political discourse is the soul of democracy, but misunderstanding and conflict can fester in divisive conversations. The widespread shift to online discourse exacerbates many of these problems and corrodes the capacity of diverse societies to cooperate in solving social problems. Scholars and civil society groups promote interventions that make conversations less divisive or more productive, but scaling these efforts to online discourse is challenging. We conduct a large-scale experiment that demonstrates how online conversations about divisive topics can be improved with AI tools. Specifically, we employ a large language model to make real-time, evidence-based recommendations intended to improve participants’ perception of feeling understood. These interventions improve reported conversation quality, promote democratic reciprocity, and improve the tone, without systematically changing the content of the conversation or moving people’s policy attitudes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Low political support for religious minority groups in the United States is often explained as a matter of social distance or unfamiliarity between religious traditions. Observable differences between beliefs and behaviors of religious minority groups and the cultural mainstream are thought to demarcate group boundaries. However, little scholarship has examined why some practices become symbolic boundaries that reduce support for religious accommodation in public policy, while nearly identical practices are tolerated. We hypothesize that politics is an important component of the process by which some religious practices are transformed into demarcations between "us" and "them." We conduct an original survey experiment in which people are exposed to an identical policy demand—women-only swim times at a local public pool—attributed to three different religious denominations (Muslim, Jewish, and Pentecostal). We find that people are less supportive of women-only swim times when the requesting religion is not a part of their partisan coalition. [ABSTRACT FROM AUTHOR]

Polarization and participation are often connected in the political science literature, though sometimes the causality runs participation to polarization and sometimes the causality runs in the reverse direction. In some accounts there is an expectation that increasing participation and increasing polarization generate an ongoing spiral effect. In this paper we evaluate the over-time relationships between polarization and participation by assessing evidence in existing panel and aggregate data. We find that people with more extreme attitudes are more likely to participate in politics. However, only one particular form of participation—persuading others—appears to predict later levels of polarization. Therefore, only persuasion has the necessary correlation and temporal ordering for a feedback loop with more extreme ideology. The implication is that the discipline should pay more attention to interpersonal persuasion as a form of participation in American politics. [ABSTRACT FROM AUTHOR]

Ideal point models have become a powerful tool for defining and measuring the ideology of many kinds of political actors, including legislators, judges, campaign donors, and members of the general public. We extend the application of ideal point models to the public using a novel data source: real-time reactions to statements by candidates in the 2012 presidential debates. Using these reactions as inputs to an ideal point model, we estimate individual-level ideology and evaluate the quality of the measure. Debate reaction ideal points provide a method for estimating a continuous, individual-level measure of ideology that avoids survey response biases, provides better estimates for moderates and the politically unengaged, and reflects the content of salient political discourse relevant to viewers' attitudes and vote choices. As expected, we find that debate reaction ideal points are more extreme among respondents who strongly identify with a political party, but retain substantial within-party variation. Ideal points are also more extreme among respondents who are more politically interested. Using topical subsets of the debate statements, we find that ideal points in the sample are more moderate for foreign policy than for economic or domestic policy. [ABSTRACT FROM AUTHOR]