I checked 7 public opinion journals on Sunday, July 12, 2026 using the Crossref API. For the period July 05 to July 11, I found 14 new paper(s) in 5 journal(s).

International Journal of Public Opinion Research

The effects of personality traits in interview time length in cellphone public opinion surveys
Ridvan Peshkopia, Don Salihu
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The rapid proliferation of mobile phone opinion surveys calls for a better understanding of their efficiency. We explain the length of mobile phone survey interviews with respondent and interviewer personality traits from the Big Five personality model. Our results show that Agreeableness (which motivates harmony seeking) and Conscientiousness (which motivates duty fulfillment) were the best predictive traits, predicting shorter and longer interview times, respectively, when they characterized both the respondent and the interviewer. Neuroticism (which motivates threat sensitivity), however, predicted opposite effects. The two other personality traits of the Big Five model, Extraversion (which motivates social engagement) and Openness (which motivates novelty exploration) did not show significant effects. Moreover, we argue that while these core psychological traits are important separately, we should pay even more attention to their interaction. Our results show that the effects of the interactions are more complex than expected and require both careful interpretation and additional research. Our findings have practical implications for the public opinion survey industry, as they could help in the training of interviewers by taking into account their personality traits. We analyze data from a public opinion survey conducted in Albania and Kosovo during the winter of 2018–2019.
From triggering reflective thinking to causing backlash: how experienced argument exposure frequency and balance affect political tolerance in the context of climate action
Quirin Y Ryffel, Thomas Zerback
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In this study, we examine if exposure to justifications for and against climate action affects citizens’ tolerance toward others. Specifically, we assume that regularly encountering pro and con climate action arguments triggers reflective thinking (i.e., internal deliberation), which then increases tolerance. Based on a 3-wave panel survey in the United States, Spain, and Germany (NW1 = 4,706; NW2 = 3,622; NW3 = 2,654), we find empirical support for the role of internal deliberation as a mediator of the effect of political argument exposure on tolerance. In line with our theoretical model, path analyses show that the frequency of experienced argument exposure fosters internal deliberation in all countries, which—in the United States and Germany—indirectly increases tolerance, depending, however, on the tolerance measure used. Overall, we do not find substantial total effects of argument exposure on tolerance in the specific context of climate action, pointing to entrenched positions. In the United States, balanced exposure can even produce backlash effects, that is, negative total effects on tolerance. Exploratory analyses indicate that climate action argument exposure effects on internal deliberation and political tolerance are highly group specific, pointing to the role of contextual factors in shaping this relationship.

Journal of Elections, Public Opinion and Parties

Ethical distance and electoral accountability: voters punish politicians whose ethical views diverge from societal consensus
Md Mujahedul Islam, Peter John Loewen
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Such a question, such an answer? An experimental study of wording effects in opinion surveys using the Swedish abortion law as a case
Henrik Friberg-Fernros, Elina Lindgren, Nora Theorin
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Implementing the mail ballot: gauging voter preferences using a discrete choice experiment
Dominic Nyhuis, Felix MĂĽnchow
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Misinformation and threats to self-interest
Mathieu Turgeon, Alessandro Freire
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Social retrospection: conversation networks, performance evaluations, and candidate support
Brett R. Bessen
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Trust in paper and convenience voting methods: evidence from the 2022 municipal elections in Ontario, Canada
Holly Ann Garnett, Nicole Goodman
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COVID-19 and performance voting: evidence from the 2021 state assembly elections in India
Subhasish Ray, Holli A. Semetko, Kiran Arabaghatta Basavaraj, Pahi Saikia, Anil M. Varughese
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Journal of Official Statistics

Estimating the Multidimensional Poverty Index in Bogotá (Colombia) Using Satellite Imagery
Juan Sebastián Oviedo, Mario E. Arrieta-Prieto
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This paper proposes an alternative approach to estimating poverty levels using satellite imagery from Bogotá, Colombia, and surrounding municipalities. Two modeling strategies are compared: Convolutional Neural Networks (CNNs) and a newly proposed extension of the Generalized Tensor Regression model based on spatially-weighted aggregations, denoted SWGTR. The models are evaluated using multiple performance metrics, including Average Accuracy (AA), Overall Accuracy (OA), Area Between Curves (ABC), Kappa Index (KI), Mean Absolute Error (MAE), and Cumulative Sums (CS), to determine their effectiveness in capturing the spatial distribution of the Multidimensional Poverty Index (MPI) at the pixel level. The application leverages PlanetScope (PS) satellite images alongside block-level data from the 2018 National Population and Housing Census (CNPV2018). The SWGTR approach demonstrates competitive performance across several metrics, particularly those accounting for the ordinal structure of poverty levels. In addition, we illustrate how the probabilistic outputs of SWGTR can be used to construct pixel-level uncertainty measures, which may inform data collection strategies by identifying regions where predictions are less certain. Overall, the findings suggest that combining spatial modeling with remote sensing data provides a promising avenue to complement traditional survey-based methods, while highlighting important limitations and directions for future research.

Public Opinion Quarterly

Welfare State Deservingness in the Era of Mass Higher Education
Sebastian Diessner, Niccolo Durazzi, Federico Filetti, David Hope, Hanna Kleider, Julian Limberg, Simone Tonelli
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The educational composition of labor markets has changed dramatically in recent decades. In many advanced democracies, the majority of workers now possess a university education. We currently know very little about how this transformation has influenced perceptions of welfare state deservingness, which are closely linked to support for the welfare state. This article addresses that gap in the literature by carrying out an original survey with a sample of 3,916 respondents from the United States. The survey combines a conjoint experiment with an information provision experiment. We find causal evidence that people are less inclined to provide welfare state assistance to the university educated than the non-university educated. This is primarily driven by need-based considerations: The university educated are seen as less in need of support, due to their strong labor market position in contemporary knowledge economies.
Are the Major “Western” Findings on Attitudes to Immigration Supported by Global Evidence? Mostly Yes, Actually
James Dennison
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The causes of variation in attitudes to immigration have been the subject of extensive scholarship in recent decades, with some factors being repeatedly evidenced. However, these findings are overwhelmingly based on studies from a small selection of “Western” countries. This article makes use of recent World Values Survey data across 66 countries globally to test how generalizable such findings on the determinants of attitudes to immigration are globally. It finds that the most typically identified sociodemographic, economic, and contextual determinants of immigration policy preferences, prejudices, and perceived effects are largely generalizable globally, namely: age, low income, job worries, rurality, not having an immigrant background, social distrust, national migration rate, feeling unsafe in one’s neighborhood, and national media bias. The effects of gender, education, and left-right self-placement are weaker and less generalizable. These findings are robust across world regions. Although the predictive power of all factors tend to be smaller outside of Western Europe, these findings shed light on the more profound, universal, and genuine nature of attitudes to immigration, their likely internal determinants—rather than purely external and contextual ones—and the global reliability of measuring their predictors.

Social Science Computer Review

Modular Polyvocality: Community Detection and Semantic Analysis of Civil Society Discourse in English-Language #MahsaAmini Twitter Networks
Hossein Masoudnia, Alireza Samiee Esfahani, Mohsen Forghani, Mohammad Moghimi, Hossein Samiei Esfahany
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This study investigates how English-language Twitter discourse surrounding the #MahsaAmini protests (October 2022–February 2023) organized into distinct yet interconnected thematic communities within civil society networks. Combining Louvain community detection, sentiment profiling (via VADER), and semantic validation (Jensen–Shannon Divergence, MANOVA, cosine similarity), we map five protest “streams” reflecting modular issue alignments, from Documentation of events involving violence and On-the-Ground Mobilization to Monarchist Advocacy & Diaspora Political Engagement and Anti-IRGC Campaigns. While these communities differ in topical focus, their shared affective tone and overlapping lexicons point to modular polyvocality, a discourse structure characterized by thematic differentiation within broader rhetorical and affective coherence. This pattern represents polyvocal consensus rather than fragmentation. We interpret these findings through the lens of connective action, networked publics, and digital diaspora activism, arguing that the discourse reflects a transnational, modular, and affectively aligned subset of protest-oriented civil society mobilization within constrained information environments. This work contributes to debates on hybrid dissent, algorithmic visibility, and the structure of protest discourse in controlled digital contexts.
Profiling Digital Hate: A Multidimensional Measurement Approach Based on the Perspective API
Thomas Kirchmair, Kevin Koban, Jörg Matthes
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The automatic classification of digital hate is a pressing challenge, yet many existing computational models remain opaque and insufficiently evaluated as measurement tools in social science contexts. This study examines the utility of Google’s Perspective API as a measurement instrument by modeling higher-order constructs of harmful discourse (i.e., incivility and intolerance) as outcomes of multiple lower-level behavioral indicators captured by distinct API scores rather than using a single aggregate score as a proxy for complex social behaviors while enabling the evaluation of state-of-the-art black-box classifiers beyond classification metrics. Drawing on 4,000 manually annotated English-language YouTube comments in the context of the Israel-Hamas war, we test whether multiple API scores predict incivility and intolerance using generalized linear and additive models, assess classification performance across non-hateful, uncivil, and intolerant content, and benchmark a recent deep learning model. Results show that Identity Attack is a strong predictor of intolerance, whereas Insult and Profanity are indicative of incivility. While classification performance is somewhat below state-of-the-art deep learning models, our approach offers important advantages: transparency, interpretability, accessibility for non-technical researchers, and potential cross-linguistic applicability. We argue that typology-driven, multi-indicator-based classification provides a practical and theoretically grounded complement to more aggregated black-box models, particularly in human-in-the-loop workflows that can help reduce annotator exposure through pre-filtering of content.