I checked 7 public opinion journals on Friday, November 06, 2026 using the Crossref API. For the period October 30 to November 05, I found 8 new paper(s) in 5 journal(s).

International Journal of Public Opinion Research

Changing abortion attitudes around the world: a within-between analysis of country-level factors
Jeffrey Mitchell, Mikael Hjerm
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There has been a renewed research interest in abortion attitudes in recent years, with a focus on country-level influences in a cross-country perspective. This research typically points to four factors at the country level that should, for different theoretical reasons, influence abortion attitudes: religiosity, gender (in)equality, political freedom, and economic prosperity. We build on this cross-sectional research by analyzing these factors longitudinally using random effects within-between models with the Integrated Values Survey in 88 countries across 32 years between 1990 and 2022. Our results show that political freedom is related to abortion attitudes, but this association exists only cross-sectionally, not longitudinally. Only changes in religiosity and GDP over time were associated with abortion attitudes, and that change in religiosity is the largest macro-level predictor included in our analysis. These results nuance previous literature on abortion attitudes and call into question whether trends in political freedom and gender equality will have the power to influence attitudes on this issue.
Political trust in crisis? An experimental panel study into the origins of subjective perceptions of structurally declining political trust
H Bey, T W G van der Meer
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The perception that political trust is in a deep crisis, driven by structurally declining trends, seems intrinsically tied to democracy. For decades, scholars responded by studying actual trends. Yet, actual trends can only partially explain the prevalence of the trust crisis narrative. Therefore, this study shifts the focus to perceived trends by comparing the actual longitudinal changes in political trust with citizens’ retrospective perceptions of these changes. We rely on observational and novel experimental data (n = 1882) from the Dutch LISS panel to investigate these incongruencies between actual trends and perceptions over a 16-year period (2008–2024). We find evidence for a bias in public opinion as past levels of trust are generally overestimated. By probing explanations from political science, sociology, and psychology, we find that this bias is primarily political rather than personal: how people remember their trust depends on whether their party governed, and reminding them who did corrects that memory.

Journal of Official Statistics

A Robust Measure of Wealth Inequality in Small Areas
Silvia Scarpa, Stefan Sperlich
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This work provides a guide to small area estimation of the quantile ratio index (QRI) to measure wealth inequality across socio-economic or geographical groups. Wealth exhibits extreme positive skewness, rendering traditional inequality measures unreliable, as they are highly sensitive to extreme values. The QRI’s robustness to this phenomenon addresses this problem, making it particularly interesting for small area estimation problems. Relying on well-established methods, we employ a logit-transformed area-level model with bias-corrected back-transformation, validate it through model-based simulations, and apply it to Italian Survey on Household Income and Wealth data. The results confirm the expected strong heterogeneity in wealth inequality across municipalities and household groups, now based on an indicator that is much more robust than the traditional ones. This work thereby highlights the practical value of combining robust inequality measurement with small area estimation techniques for the production of reliable wealth inequality estimates in policy-relevant domains.
Testing a Targeted Refusal Conversion Strategy for an Establishment Web Survey
Ellen Laupper, Ulf-Dietrich Reips
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The paper proposes a strategy for implementing an automated and interactive feature of persuasion for late and nonrespondents in establishment web surveys. The response rate for establishment web surveys is under constant pressure, and strategies to motivate small establishments are particularly costly. Consequently, the tested survey design feature, which emulates the approach of telephone follow-ups in interviewer-administered surveys, offers a potentially new, practical, and cost-effective strategy to counteract these shortcomings. A randomly selected sample of late and nonresponding establishments ( n = 3,000) of a well-established Swiss web survey of establishments was asked to indicate their primary nonresponse reason and was then randomly assigned to one of three appeal types: (1) a targeted appeal addressing their previously indicated nonresponse reason, (2) a general appeal addressing the same reasons as the study’s standard reminder letter, or (3) a main appeal addressing the main reason for nonresponse, “lack of time.” The findings suggest that the type of appeal has no significant impact on response or data quality, whereas the reason given for nonresponse and the timing within the survey period do have an impact. The implications of these findings for survey design research are discussed.

Politics, Groups, and Identities

Glottophobia in the great white north: investigating linguistic prejudice between French and English speakers in Canada
Evelyne Brie, Adrien Cloutier, FĂ©lix Mathieu, Yannick Dufresne
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Public Opinion Quarterly

Cult of Personality or Lack of Competition? Determining Trump’s Sway Over Republican Partisans on a Critical Case
Love Christensen, Mattias Agerberg
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It is well established that partisans follow their leaders. While this potentially has important implications for political accountability, studies of opinion leadership rarely examine accountability directly. We test whether partisans are able to hold a dominant leader accountable under favorable conditions using a critical case: Trump’s position on the Epstein files. Two preregistered experiments, fielded before the release of the Epstein files, show that partisans follow Trump and do not hold him accountable when he is unopposed or contested by the Democrats. However, when Trump faces opposition from within his own bloc—especially countermessaging from right-coded media elites—both issue-specific and overall approval decline substantially, and following is halved. Our results have two important implications. First, opinion leadership does not imply the absence of political accountability. Second, meaningful political competition in polarized societies may need to originate from within ideological blocs. While Trump is powerful, credible opposition can make him accountable.

Social Science Computer Review

Agreement Helps, Disagreement Matters: Scaling Framing Function Annotation With Large Language Models
Emily Robinson, Ranadheer Malla, Chico Q. Camargo
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Framing research has long relied on human annotation, but many aspects of framing – especially framing functions such as defining problems, identifying causes, making moral evaluations, or proposing solutions are difficult to label consistently and at scale. We demonstrate that large language models can reliably identify these framing functions, achieving levels of agreement comparable to those of human annotators. We evaluate three state-of-the-art models (GPT-5, Claude 3.7, and DeepSeek) alongside three trained human coders on a new dataset of social media posts about electric-vehicles, a highly topical and contested issue that holds many interpretations. This setting provides a strong test of both agreement and meaningful disagreement in framing-function annotation. Across models and humans, we find not only substantial convergence but also patterns of valid disagreement – differences that reflect legitimate alternative interpretations rather than annotation error. Recognising and preserving these cases enhances construct validity and provides a more realistic portrayal of how framing functions operate in contested debates. Our results demonstrate that large language models can scale framing function analysis while capturing the variability inherent in human interpretation, enabling forms of communication research that were previously impractical with human-only coding.
Who Goes Toxic? Understanding Political Toxicity on Social Media Across Individuals and Contexts
Alon Zoizner, Nir Lotan, Sagi Pendzel, Einat Minkov
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As political toxicity has become widespread on social media, scholars have sought to understand what drives it. While studies often emphasize platform-level explanations, such as algorithmic amplification, they offer limited insight into which users are more likely to produce toxic content. Even less is known about how the relationship between individual-level factors and toxicity varies across political contexts. Using data from 184,354 randomly sampled U.S. Twitter (now X) users, their original tweets (25M), and the accounts they follow, we link users’ inferred characteristics and political context to their real-life toxic behavior. Results show that Republican, male, and older users are consistently more likely to post both impolite and intolerant content, whereas education shows no clear association. We also find that state-level electoral competitiveness increases overall toxicity and, in some cases, conditions individual-level relationships. The age gap in impolite behavior significantly narrows in more competitive states, while the corresponding moderation for intolerance is only marginally significant. There is also some indication that partisan differences in impoliteness become larger in more competitive states, but this moderation is only marginally significant. Together, these results demonstrate that political toxicity is shaped jointly by users’ individual characteristics and the political contexts in which they operate.