I checked 7 public opinion journals on Friday, April 10, 2026 using the Crossref API. For the period April 03 to April 09, I found 12 new paper(s) in 4 journal(s).

Journal of Elections, Public Opinion and Parties

Being populist is bad for you: a six-wave longitudinal study on the relationships between populist orientation and perceived control
Michele Roccato, Silvia Russo
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Electoral outcomes versus voters' preferences: on the different tales the data can tell
Salvatore Barbaro, Anna-Sophie Kurella, Maike Roth
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Journal of Survey Statistics and Methodology

Effects of Interviewer Language and Dialect Choice on Questions About Political Trust: Examining the Asian Barometer Survey in China, the Philippines, and Indonesia
Mao Li, Victoria Owens, Fred Conrad
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This study explores how interview language affects measurement error in political trust assessments in multilingual countries, specifically China, the Philippines, and Indonesia. We propose a mechanism to explain how interview language shapes the cultural and cognitive frames respondents apply when formulating their answers, specifically evaluating its effect on trust-related survey questions. We hypothesize that interviewing multilingual respondents in their country’s official language, rather than their native language, triggers a cultural/cognitive frame that discourages the disclosure of negative opinions about political institutions. We studied China, the Philippines, and Indonesia using fourth-wave Asian Barometer Survey data, fitting a linear mixed-effects model with a matching procedure to predict trust in political institutions based on interview language. Our findings indicate that respondents in the Philippines, China, and Indonesia who were interviewed in the official language reported greater trust in political institutions than those interviewed in their native language. This phenomenon highlights the potential measurement error caused by interview language choice in cross-national and multilingual surveys, calling for greater attention from survey researchers.
Total Survey Error and the Potential Overestimation of Childhood Influenza Vaccination in the National Immunization Survey
Nicholas Davis, Tammy A Santibanez, James A Singleton, Katherine E Kahn, Yusheng Zhai
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The National Immunization Survey-Flu (NIS-Flu) monitors influenza vaccination among children in the United States, and the accuracy of NIS-Flu estimates is important for evaluating influenza vaccination programs. Total survey error (TSE) provides a framework for assessing survey accuracy. NIS-Flu data from the 2015–16 through 2017–18 influenza seasons were examined to assess components of nonsampling error: noncoverage of households by the sampling frame; household nonresponse; and measurement error resulting from parental reporting of vaccination status. We estimated the distributional parameters of each source of error from related surveys and auxiliary data and employed simulation to estimate bias. We estimated bias in end-of-season estimates of influenza vaccination rates and quantified the extent to which each source of survey error contributed to total bias. Overall point estimates (and 95 percent interval estimates) of total estimated bias were 8.0 (1.3, 14.6), 8.9 (2.6, 15.2), and 6.1 (−0.5, 12.8) percentage points for the 2015–16, 2016–17, and 2017–18 influenza seasons, respectively. Measurement error resulting from parental recall of children’s vaccination status was the largest contributor to bias; the average estimates of measurement error were 7.0, 8.2, and 5.5 percentage points for the three seasons, respectively. Errors due to noncoverage and nonresponse were relatively small (averaging <1 percentage point). Estimates of total bias were larger for Hispanic and non-Hispanic Black children and for the youngest children aged 6–23 months. Findings suggest NIS-Flu influenza vaccination rate estimates may be biased upward, primarily due to measurement error in the form of overreporting influenza vaccinations by parental respondents, with relatively little error due to noncoverage or nonresponse.

Public Opinion Quarterly

United or Divided? A Polarized Society’s Response to War
Yuval Feinstein, Geffen Ben-David
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Recent global crises have renewed interest in the rally-’round-the-flag phenomenon of public opinion. At the same time, many studies have focused on deepening political polarization across countries. This study synthesizes these two lines of research by exploring how public opinion in a deeply polarized society responds to a security crisis that, in a less polarized context, would likely have led most citizens to close ranks behind a government that declared war on national enemies. We analyzed original panel data collected in Israel before and after the October 7 Hamas attack and during the subsequent Israeli military operation in Gaza. The findings reveal a split pattern: while the vast majority of Jewish Israelis supported the war and trusted the security forces, trust in the government and prime minister remained low. The analysis further identifies two distinct sets of mechanisms of attitude. Support for the war and trust in the security forces were associated with threat perceptions and anger about the enemy’s actions. In contrast, trust or mistrust in the government and prime minister hinged on whether respondents attributed blame for the crisis to the government or to the oppositional protest movement, an assessment tied to their preexisting views on the government’s controversial “judicial reform” initiative. These results suggest that extreme political polarization can prevent the emergence of a unified rally behind governments during severe security crises, mainly when internal strife produces contested views about the government’s responsibility for the crisis.
Perceived Economic Contributions Increase Positivity Toward Undocumented Immigrants
Marco M Aviña
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Undocumented immigrants contribute to the US economy by participating in the labor force, paying taxes, and starting businesses. They do so under constant fear of deportation and while being barred from government assistance programs. Nevertheless, misconceptions about how they affect the job market and public finance persist, exacerbating animus and hindering policy reforms. Across three survey experiments and three national samples of Americans, I assess two informational interventions to increase positivity toward this group: facts, which tackle misinformation, and narratives, which foster empathy. Both interventions yield positive results overall, with anecdotal accounts of “hardworking” undocumented immigrants proving most effective among those most negatively predisposed against them. The persuasive success of economic considerations in this domain has concrete implications for policymakers and advocates in their efforts to rally public support for immigration reform and against mass deportations. Further, these findings complicate motivated reasoning accounts and suggest that belief updating is possible, provided information is tailored to the intended audience.

Social Science Computer Review

Emotional Communication Cost: The Impact of Emotional Polarization on the Effectiveness of Government Responses
Zheng Yang, Yiming Wei, Xi Lu
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Achieving online governance depends on the government’s effective response to public appeals through online political engagement. The effectiveness of the government’s response has been found to be influenced by multiple factors, including the textual characteristics of those public appeals. This article explores the impact of emotional bias and the polarization of citizens’ appeal messages on the effectiveness of government responses, through natural language processing and quantitative analysis methods of 1,553,572 public appeals on China’s Government Online Message Board on the People’s Daily website. The findings show a clear and consistent mechanism impacting the effectiveness of government response: the more polarized the emotion, the longer the time and the more words required for the government to respond, resulting in lower response efficiency. These results provide new insights for understanding contemporary digital governance, citizen digital political engagement, and online political consultation, specifically around the ‘emotional communication cost’ involved in government responses.
Crime and Calculation: The Decision-Making of Dark Web Actors
Ekaterina Botchkovar, Olena Antonaccio, Abigail Ballou, David Maimon
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Drawing on rational choice and social learning theories, this study examines how social networks, subcultural immersion, and specific activities shape dark web actors’ perceptions of risks and rewards. Using original 2024 survey data from 123 active dark web users recruited across English- and Russian-language forums and Telegram channels, we analyze how peer involvement, sustained participation in deviant subcultures, and interest in particular illicit activities influence perceived risks of detection and perceived benefits. Findings suggest that larger peer networks and deeper subcultural immersion may lower perceived risks and heighten perceived rewards. We also find important activity differences: interest in malware correlates with lower perceived risks, ransomware with higher perceived risks, and selling illicit goods with particularly high perceived rewards. These results underscore the fluid, socially constructed nature of risk and reward perceptions in clandestine spaces, offering rare empirical insight into the subjective mechanisms underlying cybercriminal choices and informing targeted prevention strategies.
Computational Evidence for the Two-Dimensional Structure of Social Evaluation: Pandemic-Era Insights From Americans’ Perceptions of Chinese and Japanese on Twitter
Xuanlong Qin, Tony Tam
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Social evaluation is fundamental to everyday interactions, yet our understanding has been constrained by fragmented theories and the lack of a scalable method for tracking group attitudes in real time. This paper resolves this methodological gap by introducing and validating a computational framework that empirically synthesizes three major theoretical models (Stereotype Content Model, Dual Perspective Model, and Semantic Differential) within a unified word embedding space. We demonstrate that social evaluation is structured by two core latent dimensions: Warmth-Communion-Evaluation (WCE), capturing affective and moral judgments, and Competence-Agency (CA), reflecting perceptions of ability and effectiveness. To validate its real-world utility, we apply this framework to U.S.-based Twitter posts about Chinese and Japanese individuals before and during the COVID-19 pandemic. Our analysis reveals that while perceptions of competence (CA) remained stable, affective evaluations (WCE) of Chinese individuals declined sharply, a dynamic not observed for Japanese individuals. This work offers a robust, scalable instrument for tracking intergroup attitudes during crises and provides a crucial bridge between social psychological theory and computational social science, enabling the real-time analysis of intergroup dynamics.
Intelligent Artifice: Machine Learning From the Middle Ages to the Enlightenment
E. R. Truitt
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This introduction outlines the contributions of the four following essays on the subject of the long history of artificial intelligence. They address the longstanding links between artificial intelligence and deception, the liberatory potential that AI offers, and the way that humans have used automata and robots to test and assert the limits of humanity. Taken together, they reveal provocative and unexpected elements of continuity with contemporary discussions about artificial intelligence and machine learning.
“Leibniz, Computing, and AI”
Audrey Borowski
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Centuries before the advent of computers, the German philosopher and mathematician Gottfried Wilhelm von Leibniz (1646–1716) sketched out a “computational ontology” whereby information operates as an organic principle imposing order, molding and driving it, in such a way that the world gains a form of consciousness, and thought produces its being at the same time as it thinks itself.
Contagion Mechanisms and Emotional Group Polarization Outcomes of Online Toxicity: An Information Ecology Perspective
Han Luo, Xiao Meng
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Drawing on information ecology theory, this study investigates how online toxicity spreads and drives emotional group polarization in Reddit discussions surrounding the Israel–Palestine conflict. Based on a dataset comprising 8,725 posts and 1,628,366 comments, we employ Google’s Perspective API to detect toxic content, BERTopic to extract discussion topics, and a dictionary-based method to measure affective features. The results show that, from the information content perspective, content related to conflict and political topics is more prone to generating toxicity. From the information user perspective, post toxicity reduces the scale of user interaction while simultaneously increasing the level of comment toxicity. Furthermore, the analysis shows that post toxicity provokes comment toxicity by triggering users’ negative or high-arousal emotions, with discrete negative emotions such as anger, sadness, and fear enhancing the positive impact of post toxicity on comment toxicity. From the information environment perspective, the results indicate that post toxicity is not significantly associated with emotional group polarization. Accordingly, post toxicity alone plays a very limited role in explaining emotional group polarization in online discussions. These findings advance our understanding of toxicity dynamics in online environments and offer evidence-based strategies for moderators, platform designers, and policymakers to mitigate harmful discourse and foster healthier online communities.