I checked 7 public opinion journals on Thursday, November 05, 2026 using the Crossref API. For the period October 29 to November 04, I found 6 new paper(s) in 4 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.

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

Investigating the Utility of LLM-Based “Silicon Samples” in Political Science Conjoint Experiments
Alice Malmberg
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This research note is the first study to explore the viability of using LLM-generated respondents in forced-choice conjoint experiments. I generate naive “silicon samples” using three models, GPT 4o-Mini, Llama 3.2, and Magistral-small-2506, benchmarked to a large sample of live conjoint respondents, allowing for direct comparisons between human and silicon choices. The resulting analyses suggest that naively generated silicon samples are insufficient to simulate human decision-making in conjoint tasks; these results also suggest that there are systemic differences in the way demographic covariates shape LLM and human choices in experimental contexts. In total, this analysis makes two important contributions: it establishes a descriptive baseline understanding of LLM-generated respondent performance in conjoint experiments and suggests that naive silicon samples cannot accurately infer human choices using demographic traits in experimental decision making contexts. Finally, the implications of these results imply that LLMs consider covariates differently than live respondents do in micro-level decision making contexts.
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.