I checked 7 public opinion journals on Monday, February 23, 2026 using the Crossref API. For the period February 16 to February 22, I found 6 new paper(s) in 3 journal(s).

Journal of Official Statistics

Privacy-Enhancing or Privacy-Elusion Technology? A Critical View of (Pseudo)Synthetic Data Based on Deep Learning
Fabio Ricciato
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In this note, we present a plausible structural mechanism by which over-parameterized deep learning models trained on real data may produce pseudo-synthetic data that constitute merely a different representation (or re-encoding) of the training data. We conjecture that, in principle, similar mechanisms may be learned by large-scale AI models even if they are not intentionally designed to do so. From there, we derive some cautionary warnings for potential adopters of pseudo-synthetic data generation tools based on deep learning. We claim that the burden of proof that no data re-encoding mechanism is at play in AI-based generation models rests with their proponents.

Public Opinion Quarterly

Hypercompetitiveness and Loser’s Consent
Marc J Hetherington, Allen Wilson, Ryan M DeTamble, David C Barker
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Electoral democracy rests on the conferral of Loser’s Consent. The Capitol insurrection of January 6, 2021—and the widespread sympathy for it that endures among Republican citizens and elected officials—can be understood as an unprecedented denial of that Consent. We hypothesize that insurrectionist sympathies among 2020 election losers are structured in part by hypercompetitiveness—a psychological need to win at all costs. Using original survey data collected in the spring of 2022, we find strong suggestive support for our hypothesis. Many of the Americans who fail to condemn the Capitol riot may not be simply knee-jerk partisans, well-intentioned victims of propaganda, or sycophants in thrall to a would-be authoritarian leader. Substantiating the anxieties of many observers, those people may instead be unwilling to abide by democratic rules if it means they have to lose.

Social Science Computer Review

Unmasking Coordination: How Inauthentic Behavior Emerged and Diffusion During the Russia–Ukraine War on Twitter
Yanhong Wu, Jianqiang Yu
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This study aims to explain the diffusion pathways of coordinated inauthentic behavior during the Russia–Ukraine conflict. A dataset of 685,491 tweets containing the hashtag #russia on Twitter was used to construct a coordination network based on textual similarity and time synchronicity. By identifying leader-follower relationships, analyzing hourly time slices, and analyzing evolution metrics, four key insights were revealed. First, leaders constitute a stable core with an average of about 1741 nodes while peripheral followers fluctuate substantially, indicating a resilient core-peripheral structure. Second, diffusion advances across multiple fronts rather than remaining within single communities, with 67.05% of leader-follower ties crossing content clusters and the top 30 leaders posting across an average of 7.1 clusters and up to 9. Third, apparent synchronization is not driven by posting density alone but arises from rhythmic coupling between leaders and followers, as followers respond after an average delay of about 30 min and cluster peaks typically occur within less than 1 hour of each other. Fourth, diffusion capacity is not released once and for all but regenerates along a trajectory that moves from concentration to multiploidization and then to restructuring. Based on the results, we conceptualize coordinated inauthentic behavior as a strategically adaptive system with regenerative properties and provide governance implications.
Polio Beyond the Drop: Rethinking Vaccine Hesitancy in Urdu Tweets Beyond Western Behavioral Models
Muhammad Awais
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Pakistan is one of the few remaining countries where wild poliovirus remains endemic, despite decades of eradication campaigns. Yet, vaccine hesitancy persists, not merely due to biomedical skepticism but through digital discourse. Drawing on 6,399 Urdu language tweets, this study uses natural language processing and lexicon based modeling to test four hypotheses on the emotional and symbolic drivers of hesitancy. Emotions are operationalized using the NRC Emotion Lexicon, treating trust and fear as measurable affective signals. The findings challenge Western behavioral models such as the Extended Parallel Process Model (EPPM): trust is negatively associated with hesitancy, whereas fear is positively associated with it even when trust is present. Notably, fear’s effect weakens in security framed tweets, which express moral resolve and collective strength rather than panic. Religious framing also predicts hesitancy, but it is often based on misquoted or misinterpreted religious references. In many cases, such discourse misaligns with the actual teachings of the religion, which historically endorse disease prevention and public health. Vaccine hesitancy in this context emerges not as an individual risk judgment, but as a culturally embedded form of communicative resistance, requiring discourse based, context sensitive approaches to global health communication.
Online Polarization and Violence in the United States: A Computational Analysis of Viewer Comments on Partisan News Coverage of President Donald Trump’s First Assassination Attempt
Dennis Ekwemnachukwu Okeke, Margaret Adutwumwaa Boateng, Chibuzor Peter Okpala, Christiana Ibiwoye, Anthony Obi Okeke, Ousman Mbaye, Isaac Ebuka Obah
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This study analyzes 15,000 user comments from ideologically distinct news platforms—Fox News (right-leaning), ABC News (left-leaning), and BBC News (centrist)—to investigate how political ideology shapes emotional expression and engagement following the July 2024 assassination attempt on President Donald Trump in Pennsylvania. We examine whether right-leaning comments exhibit elevated anger and hate speech, whether left-leaning comments show greater empathy and positive affect, and how emotional intensity influences engagement metrics such as likes and shares. Using computational science methods, we trace the evolution of emotional tone and toxicity over time, focusing on affective polarization triggered by a high-salience violent event. Our findings reveal that ideological alignment amplifies engagement with congruent emotional content, reinforcing echo chambers and intensifying online hostility. We address gaps in understanding how ideological echo chambers amplify polarized emotions and online hostility following a high-profile violent event, emphasizing the place of emotions in contemporary political communication and digital news consumption.
Effects of Social Media Addiction on Critical Thinking: The Mediating Role of Executive Function
Mehdi Reza Sarafraz, Nasim Nazari, Aida Peyvandi
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Recently, social media addiction (SMA) has increased globally. Like other addictions, it can have detrimental effects on individuals. Despite growing concerns, limited research has explored the relationship between SMA and critical thinking (CT) and the mechanisms underlying this connection. Therefore, this study investigated this relationship by focusing on executive function (EF) as a potential mediator. A survey using the Internet Addiction Test-Social Networking Sites (IAT–SNS) version, Critical Thinking Disposition, and Behavior Rating Inventory of Executive Function—Adult Version (BRIEF-A) was conducted with 307 Iranian young adults aged 18 to 40 years (M = 23.35, SD = 4.45; 72.6% female). The findings revealed a significant positive association between executive dysfunction and SMA and a significant negative association between executive dysfunction and CT. Structural equation modeling indicated no direct correlation between SMA and CT; rather, the relationship was mediated by EF, with only the indirect effects via EF being statistically significant. These results suggest that executive dysfunction acts as a risk-enhancing mediator in the relationship between SMA and CT. Further experimental studies are required to gain a more comprehensive understanding of these associations. Interventions should be developed to mitigate the negative effects of SMA on EF and CT.