I checked 7 public opinion journals on Saturday, June 13, 2026 using the Crossref API. For the period June 06 to June 12, I found 10 new paper(s) in 5 journal(s).

Journal of Elections, Public Opinion and Parties

Adapting to the radical right rival? examining legislative speeches in response to SD's electoral success
Esther Mary L. Calvo
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Journal of Official Statistics

Beyond Survey Length: Understanding Respondent Perceptions of Burden
Erica C. Yu, Brandon Kopp, Victoria R. Narine
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Respondent burden is complex and represents more than just the time spent completing the survey. With this research, we highlight the importance of looking beyond objective measures to understand the respondent’s perception of the survey-taking experience. First, we asked participants to tell us in their own words about the term “burden” and how surveys and survey questions can be burdensome. We identified themes in how respondents think about surveys that can inform targeted approaches to reducing respondent burden. We then tested the idea that perceptions of burden may not adhere to any objective measure of what it means for a survey to be burdensome. Through survey instructions, we presented different frames of reference to dissociate perceptions of survey length from actual survey length and analyzed the effect on ratings of burden. Our research suggests that factors like repetition, disorganization, and perceptions of pointlessness are key to respondents’ understanding of burden. Different frames of reference translated to significant differences in both perceptions of survey length and perceptions of burden, regardless of actual survey length. To improve respondents’ survey-taking experience, survey designers must go beyond survey length to consider perceived burden.
Book Review: Robust Small Area Estimation: Methods, Theory, Applications, and Open Problems , by Jiming Jiang and J. Sunil Rao JiangJimingRaoJ. Sunil. Robust Small Area Estimation: Methods, Theory, Applications, and Open Problems. 2025Boca Raton, FL: Chapman & Hall/CRC. ISBN 9781032488851, 275 pages.
Andreea L. Erciulescu
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Politics, Groups, and Identities

Theory and action: an exploration of civically engaged research, environmental justice, and political theories of democracy
Curtis Kline, Rosa Castillo Krewson
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Public Opinion Quarterly

Causal Beliefs and the Potential for Political Backlash Against AI
Sophie Borwein, Beatrice Magistro, R Michael Alvarez, Bart Bonikowski, Peter J Loewen
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Artificial intelligence is poised to reconfigure the economy and politics. Although new technologies often produce net economic gains, their costs and benefits are unequally distributed, making them susceptible to politicization. We argue that whether and how AI becomes mobilized for partisan gain will depend on the public’s causal beliefs about the winners and losers of AI. We categorize these causal beliefs into four types using a novel survey instrument fielded with approximately 6,000 Americans and Canadians. Using latent class analysis, we show that while some respondents are supportive of AI, a significant portion of the public theorizes it as a threat—harming consumers and replacing rather than complementing workers’ skills. These beliefs are aligned with political preferences, predicting support for policies that delay job loss over those that help workers adapt, and polarizing voters along existing partisan lines. We conclude that fissures in the public’s attitudes toward AI already exist and are primed for exploitation by political entrepreneurs.
Does Compulsory Voting Improve Democratic Attitudes and Engagement? Quasi-Experimental Evidence from Belgium
Dieter Stiers, Shane P Singh
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For over a century, scholars, policymakers, and advocates have argued that compulsory voting not only boosts turnout but also promotes prodemocratic attitudes and political engagement. However, empirical tests of this claim have been limited by trade-offs between internal and external validity. To address this challenge, we investigate the consequences of compulsory voting for democratic attitudes and engagement using a design that yields relatively credible and generalizable estimates. We exploit a unique quasi-experiment in Belgium, where compulsory voting was recently abolished for local elections in one region but retained elsewhere. To estimate the effects of this policy change, we employ difference-in-differences models using a new panel dataset covering four elections held in 2024. We find that, although the abrogation of compulsory voting caused a sharp drop in turnout, it did not alter democratic attitudes and engagement.
Do Daughters Change Their Fathers? Evidence from the First-Daughter Effect in Japan
Daina Chiba, Yoshikuni Ono
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Family experiences—especially the gender of one’s children—can reshape parents’ political attitudes, challenging traditional models of political socialization. Existing research in advanced democracies documents the “first-daughter effect,” whereby fathers of first-born daughters express more egalitarian views on gender roles. However, evidence from more conservative or non-Western contexts remains scarce and inconclusive. This study examines whether the first-daughter effect occurs in Japan, a country characterized by stable democratic institutions but enduring gender inequality. Using nationally representative survey data from 2000 to 2018 and leveraging the quasi-random assignment of first-child sex, we show that Japanese fathers with first-born daughters exhibit more gender-egalitarian attitudes. They also express greater support for gender-equality policy reforms, such as dual-surname legislation. These effects are confined to gender-related domains and do not extend to broader political ideology or non-gender-related policy preferences. Our findings contribute to research on reverse political socialization by demonstrating that raising daughters can reshape core political attitudes, even within culturally conservative settings. This suggests that private family dynamics may serve as an underrecognized but powerful mechanism for promoting gender equality in public opinion.
The Intersectional Anger Gap: How Race and Gender Condition the Impact of Anger on Participation
Sara Morell, Marzia Oceno
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Political science research has long demonstrated a relationship between anger and political participation, a relationship that is likely to significantly vary across intersectional groups. While anger has increased overall, Black men and women have generally reported lower levels of anger and a weaker relationship between anger and electoral participation than white men and women. However, because of variations in both historical and contemporary access to political voice, as well as legacies of state violence, we expect not only race but also gender to moderate the relationship between anger and both electoral and non-electoral participation. Taking an intersectional approach across two nationally representative surveys, we demonstrate that both race and gender impact the association between anger and electoral as well as non-electoral engagement. We do find that race explains men’s differences in political participation. However, when looking among white and Black women, we find that white women engage in non-electoral participation and Black women engage in electoral participation at higher rates than previous work would predict. In 2020, Black women, along with both white men and women, when angry, were mobilized to participate electorally. Further, while Black women were mobilized by anger to protest in 2020 and 2022 and Black men were mobilized to sign petitions and post online in 2022, anger motivated white women’s non-electoral engagement in both 2020 and 2022. This research informs our understanding of the circumstances through which anger is mobilizing, highlighting, in particular, the breadth of avenues to political engagement taken by white and Black women.
Polling Across Borders: The Promise and Pitfalls of Convenience Samples in a Cross-National Context
Dino P Christenson, Gustavo A Flores-Macias, Sarah E Kreps, Douglas L Kriner
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This study evaluates the performance of two widely used survey platforms, Lucid and Morning Consult, across six diverse national contexts: Brazil, India, Japan, Nigeria, the Philippines, and the United States. We assess the impact of platform choice on sample composition, response quality, political judgments, and treatment effect estimation, focusing on a randomized corruption treatment embedded within the survey. Attention filter passage rates were similar and generally high across countries and platforms, while the percentage of high-frequency survey-takers varied greatly across countries. Our findings reveal significant demographic skews, with both platforms consistently overrepresenting college-educated respondents. Despite these differences, political assessments and estimated average and heterogeneous treatment effects remain broadly consistent across platforms, and in Brazil our estimates largely tracked those from past research with a probability sample. We find some evidence of cross-national variation in the magnitude of treatment effects, but these differences were often platform-specific. These results suggest that convenience samples can provide reliable estimates of causal effects even in diverse contexts. Taken together, our research highlights the trade-offs between cost, speed, and representativeness in global public opinion research, offering insights into the challenges and opportunities of online survey platforms.

Social Science Computer Review

Computational Public Opinion Measurement: A Systematic Review of Methods and Methodological Limitations
Eun Gyo Joung
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Computational Public Opinion Measurement (CPOM) uses natural language processing and machine learning to infer public attitudes from social media. However, the methodological foundations and validity constraints of CPOM remain inadequately documented. This systematic literature review examines the computational methods, sentiment representations, and measurement strategies employed in CPOM research, and identifies the structural and methodological limitations that constrain CPOM as an approach to measurement. This systematic review (PRISMA, 2020) searched seven databases, identifying 56 studies from 5,108 records (2008–2025). Methods shifted substantially across eras: lexicon-based approaches declined from 77.8% (2011–2015) to 28.6% (2021–2025) while deep learning grew from 0% to 40.0%. Despite this technical evolution, empirical validation remains rare. Only 11 of 56 studies (19.6%) computed a quantitative statistic against an external benchmark such as a survey or poll. In other words, methodological sophistication and validation rigour moved in opposite directions: none of the 14 deep learning studies computed a quantitative validation statistic, while all 11 that did used lexicon-based or classical machine learning. The broader field shows an even larger gap: 49.4% of all full-text-assessed papers were excluded for providing no external validation or representativeness analysis at all. The recognition-action gap widened over time: TSE awareness grew from 44.4% to 80.0% across eras while quantitative validation rates fell from 33% to 11%, showing that growing awareness has not translated into action. CPOM must move beyond technical sophistication toward systematic criterion validation, demographic adjustment, and transparent reporting.