I checked 7 public opinion journals on Wednesday, April 15, 2026 using the Crossref API. For the period April 08 to April 14, I found 9 new paper(s) in 3 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

Analysis of Risk and Protective Factor Surveillance for Noncommunicable Diseases Using A MultiMode Data Collection Approach
Laura Cordeiro Rodrigues, Izabella Paula AraĂșjo Veiga, LetĂ­cia De Oliveira Cardoso, Rafael Moreira Claro
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Telephone surveys have faced challenges, including declining response rates and increasing costs, prompting the need for alternative data collection methods. Multimode data collection has emerged as a viable approach, producing high-quality data while reducing costs and improving response rates. This study evaluated the feasibility of a multimode data collection approach for the surveillance of risk and protective factors for noncommunicable diseases in Brazil, assessing its impact on response rates, costs, and data quality compared to a telephone only survey. In the multimode study individuals were invited via Short Message Service to participate in an online survey, and non-respondents, after reminders, were subsequently contacted by telephone. Data from the multimode study were compared with telephone-only interviews conducted using the Vigitel 2023 methodology. Design weights were applied to adjust for differential nonresponse, and final calibration weights aligned the sample’s sociodemographic composition with the target population through the raking method. The sociodemographic composition of the samples was compared. The prevalence of 23 health indicators was compared between the samples. Method effect was defined as the percentage variation in indicator prevalence between the multimode and telephone-only, reflecting differences attributable to data collection mode. Performance indicators, including eligibility rate, response and refusal rates, average interview cost, and interview duration, were also assessed. The multimode study showed sociodemographic similarity to the target population. Among 23 health indicators, only one differed between surveys (weighted comparison). Three indicators (passive smoking at home, e-cigarette use, and negative self-rated health) showed high method effect values, and one (obesity) was negatively associated with the multimode study (Prevalence Ratio: 0.68, 95 percent CI: 0.56–0.82). These findings indicate that multimode data collection is a viable option for the surveillance of risk and protective factors for noncommunicable diseases, yielding prevalence estimates comparable to those from telephone-only surveys, with greater operational speed and slightly lower cost.
Respondent-Driven Sampling Online (Web Rds) as a Strategy to Access Hard-To-Reach But Non-Hidden Populations: The Case of Health Professionals Working in Chilean Schools
Katherine Dinamarca-Aravena, Andrés Gonzålez Santa Cruz, Sonia Morales Miranda, Teresita Rocha Jiménez, Álvaro Castillo-Carniglia
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Respondent-driven sampling (RDS) is a method commonly used to access hidden or hard-to-reach populations through peer referral chains. This study examines the application of Web RDS to a population of education support professionals (PEAs) working in inclusive school settings in Chile. Although this population is socially visible, they lack a defined sampling frame and are difficult to access due to regulations that restrict the dissemination of contact information for professionals in the Chilean school system. We used a sequential exploratory mixed-methods design. The formative qualitative phase included interviews with 52 PEAs and guided seed selection and recruitment strategy design. The quantitative Web RDS survey reached 474 participants across 11 recruitment waves. Diagnostic indicators—equilibrium, homophily, recruitment depth, and network-size stability—were calculated to assess whether key RDS assumptions were reasonably met. Additional control mechanisms included institutional email validation, unique access codes, and a set of internal eligibility questions that prevented respondents from continuing the survey when their answers did not meet the study criteria (e.g. confirming current employment in a school and active work as an education support professional). Findings show that Web RDS can be successfully adapted to semi-visible institutional populations beyond its traditional applications in sociological and public health research. The study also provides a transparent discussion of potential sources of error and practical considerations for applying RDS in professional educational contexts.
Developing A Customized, Enumeration Area-Based Sampling Frame Tailored to a Specific Population Subgroup Using Geospatial Methods
Sarchil Qader, Edith Darin, Ahmadou Hamady Dicko, Hisham Galal, Hyunju Park, Rebeca Moreno Jimenez, Andrew Harfoot, Andrew J Tatem
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A national sampling frame typically comprises a list of Primary Sampling Units (PSUs), such as enumeration areas derived from census data, which are commonly used in household surveys. Both national statistical offices and non-governmental organizations often rely on this framework when conducting surveys related to forced displacement. However, these frames are generally developed without considering the estimated number or geographic distribution of displaced populations. As a result, achieving the desired sample size becomes difficult and cost-intensive, as selected units frequently contain no individuals of interest. This study aimed to evaluate the potential of geospatial methodologies to develop a digital national sample frame tailored to a specific population subgroup or the general population, with the goal of ensuring applicability across diverse settings. For the first time, this work produced publicly accessible, digitized boundaries for urban and rural areas in Cameroon that are aligned with official administrative divisions and do not follow a grid-based system. According to our classification and estimated number from the ProGres database, 46 percent of refugees in Cameroon resided in rural areas, while 31 percent lived in camps and 23 percent in urban settings. The proposed geospatial approach offers a cost-effective alternative to traditional manual methods, particularly in data-scarce environments, and eliminates common geometric inconsistencies found in manual mapping efforts. All sampling units were nested within administrative boundaries, and in populated areas, their delineations aligned with observable ground features and respected major physical barriers. Importantly, including the refugee population in the customized national sampling frame was essential, as it enhanced the representativeness of refugees within it. This approach can be easily adapted to other countries. Notably, it was implemented in preparation for 2024’s Forced Displacement Survey in Cameroon, highlighting its practical application and relevance in real-world survey contexts.

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.
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.