I checked 7 public opinion journals on Monday, March 30, 2026 using the Crossref API. For the period March 23 to March 29, I found 8 new paper(s) in 4 journal(s).

International Journal of Public Opinion Research

WAPOR News 2025
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Journal of Survey Statistics and Methodology

The Extended Crosswise Model Adjusted for Random Answering
Khadiga H A Sayed, Maarten J L F Cruyff, Andrea PetrĂłczi, Peter G M Van der Heijden
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The Extended Crosswise Model is a popular randomized response design that employs a sensitive and innocuous statement, and asks respondents if one of these statements is true, or if none or both are true. Although the model has a degree of freedom, it is unable to detect random answering. In this article, we propose a new method to detect and correct for random answering. This method makes use of a non-sensitive control statement and a quasi-randomized innocuous statement to which both answers are known, which allows for the detection of and correction for random answering. A simulation study shows that this method yields unbiased estimates of the prevalence of sensitive attribute. For four surveys among elite athletes, we present prevalence estimates of doping use that are corrected for random answering.
Text Messaging as a Survey Interviewing Mode: A Deeper Look
Frederick G Conrad, Andrew L Hupp, Christopher Antoun, H Yanna Yan, Michael F Schober, Makenna Harrison
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Text message communication is nearly universal—at least in many places—but its use for collecting survey data is still emerging. Respondents in text message interviews have been shown to satisfice less than respondents in telephone (voice) interviews, and when given a choice, opt to be interviewed by text more than by voice. This study explores how the properties of text message communication work together to produce these and other advantages for text message surveys: texting is asynchronous, allowing respondents to invest as much time as needed to answer thoughtfully; text messages are persistent, that is, remain available until it is convenient to respond; and text messages are noticeable, that is, various notifications increase respondents’ awareness they have been texted. The data in the current study were collected in two text and two voice interview modes, distinguished by whether a human or automated agent asked the questions. A higher proportion of invited sample members started text than voice interviews, reflecting the noticeability and persistence of the invitations. Text interviews took longer to complete than voice interviews, and text interview response times (RTs) were more variable, suggesting that respondents were able to invest more thought in their answers as warranted by particular questions. We attribute the reduced satisficing previously observed in text interviews to asynchrony: as RTs increased, the frequency of rounded numerical answers (one satisficing indicator) decreased; the opposite pattern was observed for voice interviews. Although individual text interviews were longer than corresponding voice interviews, the overall field period was shorter for text. We show this is due to much quicker recruitment for text than voice interviews, which we attribute to the noticeability and persistence of texted invitations. The result is that data from text interviews can be released quickly, suggesting the mode may be well-suited for time-sensitive measurement. The findings make a strong case for considering text interviewing when designing a study.

Public Opinion Quarterly

Thinking Ideologically: The Limited Role of Left and Right Labels as Policy Shortcuts
Sarah Lachance, Clareta Treger
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How do voters use left-right ideological labels as shortcuts for policy positions in evaluating electoral candidates? We offer a distinction between maximal and minimal forms of ideological thinking. While maximal thinking implies that voters rely on ideological proximity as a proxy for policy congruence with candidates, minimal thinking requires only that voters use ideological labels to infer candidates’ positions—even if their own ideological identification is inconsistent with policy preferences. Drawing on original experimental data from Canada (N = 1,087)—a multiparty system with a fluid ideological landscape—we find that voters’ ideological self-placement is often misaligned with their policy positions, especially among right-leaning individuals. However, voters still use ideological proximity to infer candidates’ policy stances in the absence of policy information, supporting the Minimal Theory. These findings contribute to theories of political decision-making beyond the United States and have implications for substantive representation in systems with centrist or ideologically flexible parties.
Americans’ Responses to COVID-19 and the Conditional Role of Dispositional Needs for Security: A Replication and Extension
Adam R Panish, Trent Ollerenshaw, Joseph A Vitriol
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A predominant theory in political psychology proposes that the political right is more threat sensitive than the political left due to differences in needs for security. Yet, in a prototypical case study of the threat-politics link, Americans’ responses to COVID-19 deviated from this expectation. In this research note, we demonstrate that this phenomenon can be understood by accounting for the role of partisan sorting and political engagement in translating dispositional needs for security into political preferences. Across six national surveys (combined N = 8,687), we demonstrate that psychological needs for security are indeed associated with protective COVID-19 responses, but only among politically disengaged Americans. For politically engaged Americans, increased security needs were associated with sorting into right-wing discourses that, in turn, promoted lax COVID-19 responses. Our findings demonstrate that dispositional security needs conditionally affect political attitudes but cast doubt on the claim that attitudinal differences will necessarily manifest behaviorally.
Scaling Open-Ended Survey Responses Using LLM-Paired Comparisons
Matthew R DiGiuseppe, Michael E Flynn
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Survey researchers rely heavily on closed-ended questions to measure latent respondent characteristics like knowledge, policy positions, emotions, ideology, and various other traits. Closed-ended questions are easy to analyze and collect, but necessarily limit the depth and variability of responses. Open-ended responses allow for greater depth and variability in responses, but are labor intensive to code. Large language models (LLMs) may help with this problem, but existing approaches to using LLMs have a number of limitations. In this paper, we propose and test a pairwise comparison method to scale open-ended survey responses on a continuous scale. The approach relies on LLMs to make pairwise comparisons of statements that identify which statement “wins” and “loses.” With this information, we employ a Bayesian Bradley-Terry model to recover a “score” on a latent dimension for each statement. This approach allows for finer discrimination between items, reduced anchoring bias, better measurement of uncertainty, and is more flexible than methods relying on Maximum Likelihood Estimation techniques. We demonstrate the utility of this approach on an open-ended question probing knowledge of interest rates in the US economy. A comparison of six LLMs of various sizes reveals that pairwise comparisons show greater consistency than zero-shot 0–10 ratings across a variety of model sizes. Further, comparison of pairwise decisions is consistent with knowledgeable crowdsourced workers.

Social Science Computer Review

Online Information Exposure, Knowledge Development, and AI Anxiety: Extending the Cognitive Mediation Model to Explain GenAI Adoption in China
Lunrui Fu, Mulin Jiang, Bo Hu, Yunsong Li
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As Generative AI (GenAI) becomes increasingly integrated into everyday information environments, many individuals remain uncertain or reluctant to engage with these technologies despite their growing potentials. By extending the Cognitive Mediation Model (CMM), the study emphasizes the cognitive and emotional processes that underpin public engagement with emerging digital information technologies. A survey of 527 Chinese participants was conducted using an online questionnaire. Structural equation modeling was employed to test the extended CMM, examining the relationships between exposure to traditional and social media, elaboration, interpersonal communication, knowledge acquisition, anxiety, and GenAI usage intention. Results indicate that exposure to both traditional and online social media were positively associated with elaboration and interpersonal communication, which were in turn negatively associated with anxiety and positively associated with perceived knowledge. However, elaboration does not significantly relate to factual knowledge, while interpersonal communication was negatively associated with factual knowledge. Anxiety was negatively associated with GenAI usage intention, whereas factual and perceived knowledge were positively associated with adoption. Theoretical and practical contributions are discussed as well.
Evolution of Deep Learning Models for Misinformation Detection in Social Media Textual Data: Background, Architectures, Datasets, and Emerging LLM Applications
Ziad Elgammal, Reda Alhajj
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With the exponential growth in social media usage, the rapid spread of misinformation has become a critical global challenge. Recent advances in large language models (LLMs) have shown promising potential in automated misinformation detection. This survey provides a comprehensive review of LLM-based approaches for detecting misinformation in textual data on social media platforms. In this work, we analyze 70+ recent papers; to examine the evolution, implementation, and effectiveness of various LLM architectures in this domain. Our analysis reveals that BERT-based models dominate the field, appearing in approximately 85% of studies, with domain-specific variants like CT-BERT demonstrating superior performance in specialized contexts such as COVID-19 misinformation detection. We provide detailed comparisons of model architectures, implementation strategies, and performance metrics across different domains. Additionally, seven major datasets commonly used in this field were analyzed, examining their characteristics, limitations, and suitability for different detection tasks. The survey also addresses key challenges, including linguistic nuances, model interpretability, and ethical considerations. Our findings indicate that while LLM-based approaches achieve impressive accuracy metrics, significant challenges remain in cross-domain generalization and real-time detection. This survey concludes by identifying promising research directions and providing recommendations for robust model evaluation frameworks.