I checked 7 public opinion journals on Saturday, June 20, 2026 using the Crossref API. For the period June 13 to June 19, I found 4 new paper(s) in 3 journal(s).

Journal of Survey Statistics and Methodology

A Pathological Investigation of Questionnaire Translation: Type, Frequency, and Severity of Questionnaire Translation Errors Made by Iranian Researchers
Nasimeh Nouhi Jadesi, Marziyeh Sadeghzadeh
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The present study investigated and assessed translation errors committed by Iranian researchers in the process of translating questionnaires from English into Farsi (Persian) (the official language of Iran) within the context of questionnaire validation studies. Employing a measurement equivalence/invariance analysis, this research applied the evaluation framework proposed by Behr (2023) to compare the original English versions of questionnaires with their translated Farsi counterparts, based on the premise that fewer errors correlate with higher translation quality. The dataset for evaluation was compiled through systematic database searches, focusing on questionnaires on women. A total of 14 questionnaires meeting the inclusion criteria, comprising 292 items, were identified. Two researchers independently compared and evaluated the quality of the source and translated versions of each questionnaire, followed by online sessions in which they engaged in dialogic discussions to share their coding and perspectives. The questionnaires were examined for translation errors, and the frequency of each of the seven error types outlined in the framework was quantified, exemplified, and analyzed. Findings revealed that the majority of errors (88 percent) occurred at the linguistic level, encompassing general translation issues such as semantic accuracy, stylistic appropriateness, and grammatical correctness, whereas fewer errors pertained to questionnaire-specific translation challenges, including cultural adequacy, terminological consistency, and layout/presentation. Additionally, the study discussed the possibility of introducing some refined manifestations of error types mentioned by the framework. Based on these findings, practical recommendations were provided for researchers involved in questionnaire translation and for journal reviewers and editors overseeing such work.

Public Opinion Quarterly

(Mis)information, Polarization, and Trust in Elections: Longitudinal Evidence from Canada
Mathieu Lavigne, Holly Ann Garnett, Aengus Bridgman
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Scholars have suggested that we are experiencing a global crisis in trust in elections, with political polarization and misinformation often cited as reasons for distrust. However, empirical evaluations of perceptions of election integrity over time remain scarce. Using data from the Canadian Election Study from 2008 to 2021 and the Media Ecosystem Observatory from 2021, we show that trust in electoral management bodies, measured in terms of confidence, satisfaction, and fairness, has significantly declined since 2019 among Canadians with a right-wing ideology or who support right-wing parties. We find evidence that rising affective polarization, right-wing and social media consumption, and belief in US-based election fraud narratives are associated with significant trust declines in electoral integrity. The results identify key factors linked to the recent politicization of citizens’ perceptions of election fairness and have important implications for the health of our democracy.
Disentangling the Three Facets of Mass Ideological Polarization: A Network Approach Across 78 Societies
Yufan Guo, Yilang Peng, Tian Yang
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Public opinion research has intensively examined mass ideological polarization, often linking it to outcomes that threaten key democratic ideals. Both theory and empirical evidence suggest that it comprises multiple manifestations, with their relationships, however, remaining largely unclear. Meanwhile, previous studies have seldom conducted cross-contextual comparisons, which are essential for understanding how macrostructural factors shape mass polarization. These limitations partly arise from the methodological challenges that hinder consistent measurement across diverse contexts. To fill these gaps, this study introduces a belief network approach to compare three core manifestations of mass ideological polarization: disagreement, symbolic-operational ideological alignment, and within-operational ideological alignment. Using data from 78 societies in the World Values Survey and the European Values Study (2017–2023), we employ improved network-based measures that capture ideological alignment without relying on context-specific assumptions. Surprisingly, our findings reveal substantial cross-national variation and contrasting correlations among these manifestations, indicating that mass ideological polarization is neither singular nor universal. We further apply the network approach to examine how modernization, a critical macrostructural factor, shapes ideological polarization, showing that it predicts lower disagreement but higher ideological alignment. By offering a scalable and context-adaptable tool for comparative public opinion research, this study proposes an expanded theoretical framework of mass polarization, highlighting the need to distinguish its diverse manifestations and their structural drivers across global contexts.

Social Science Computer Review

From Fragmented Narratives to Systemic Critiques: Long-Term Transformations in Korean Twitter Discourse on Sexual Violence
Ji-Myoung Choi, Hye-Won Choi, Chico Q. Camargo
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This study examines how the #MeToo movement reshaped long-term public discourse on sexual violence in South Korea. While research has documented the surge of attention following prosecutor Seo Ji-hyun’s 2018 disclosure, little is known about how the movement reorganized the broader discursive field beyond the initial moment of crisis. Drawing on discursive institutionalism, we argue that #MeToo acted as a discursive shock that consolidated previously fragmented conversations into more coherent and durable interpretive frameworks. Using an 8-year corpus of 351,582 Korean-language tweets (2015–2023), we apply Dirichlet Multinomial Regression topic modeling and time-series analysis to identify shifts in both topical content and discursive structure. Our findings show a transition from episodic, emotion-driven narratives to stable, thematic frames emphasizing human rights, systemic discrimination, and institutional accountability. We further demonstrate increasing convergence between social media and mainstream news, indicating diffusion and stabilization of these new frames. The results suggest that digital activism can produce enduring transformations in public meaning, illuminating how social movements institutionalize discourse over time.