đŸ€– Must-Read Articles đŸ€–

Experimental Feature

Even just looking backwards a week, there are a lot more articles published than most of us could hope to read. We can always skim the titles and abstracts ourselves, but I wanted to test out some automation. The articles below, all of which can be found elsewhere on this site among other new publications, were chosen by Google Gemini Flash Thinking as "must-read" articles. The proper criteria are in the eyes of the beholder and Gemini doesn't apply my criteria without error. I may continue to refine the prompting based on experience and feedback. Like the rest of the site, this will update daily!

Global divides in academic knowledge production: mapping social media and society research
Annals of the International Communication Association
Muhammad Awais
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In the past two decades, research on social media and society has grown into a major interdisciplinary field, analyzing how identity, politics, and knowledge circulate through digital platforms and reshape global communication. Yet as the field expands, questions remain about its intellectual structure and global inclusivity. This study offers a dual contribution: first, it uses topic modeling and natural language processing to identify dominant research themes in abstracts of over 9,000 peer reviewed articles on social media and society. Second, it examines citation disparities based on author affiliation and international collaboration. Results reveal a thematic concentration on identity, activism, and misinformation but also a citation hierarchy favoring authors from OECD countries, with no consistent advantage for international collaborations. Importantly, the study finds that even when funding is held constant, OECD based authors receive disproportionately higher citations, suggesting that epistemic inequality stems from deeper structural and symbolic hierarchies. The study contributes to the field by combining bibliometric and critical methods, offering a reflexive, data driven account of global divides in who produces and who receives credit for knowledge in the digital age.
NLP-driven analysis of linguistic markers, readability, and textual features in communication retractions using COPE guidelines
Annals of the International Communication Association
Md Saidur Rahman Khan, Han Woo Park
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This study investigates linguistic and textual patterns in retracted communication studies to identify potential markers of research integrity breaches. Analyzing 367 retracted articles from the Retraction Watch database, classified using Committee on Publication Ethics (COPE) guidelines, it employs computational linguistics to examine the relationship between discursive markers and COPE-classified misconduct. Specifically, it investigates whether retracted abstracts exhibit lower readability or lexical diversity, indicating epistemic instability. Employing SSCI-SciBERT embeddings, dimensionality reduction, and density-based clustering, the study assesses semantic structure across retraction categories. Readability indices, lexical diversity, and syntactic features were compared against a matched corpus of 367 highly cited, nonretracted Web of Science articles. Results indicate that retracted abstracts exhibit reduced type-token and hapax legomena ratios, increased named entity density, and higher sentence counts. Multivariate analyses reveal statistically significant differentiation across COPE-aligned categories, particularly in subjectivity and booster usage. Supervised classification models, notably Support Vector Machine and Logistic Regression (cross-validated AUC = 0.90), achieved robust discrimination between retracted and nonretracted texts within the sampled corpus, demonstrating the predictive power of abstract-level linguistic features alone. Interpreted through Hyland’s stance framework and Information Manipulation Theory, findings suggest that rhetorical imbalance and textual opacity are statistically associated with retracted communication abstracts, warranting further theoretical investigation.
Couples’ Coparenting and Parent-Child Relationship Quality: A Dyadic Daily Diary Study
Journal of Social and Personal Relationships
Naomi Downes, Giulia Spagnulo, Laura M. Vowels, Joëlle Darwiche
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This study examined how daily experiences of coparenting relate to parent–child relationship quality using a dyadic daily diary design. Eighty-five heterosexual cohabiting couples (N = 170 individuals), with at least one child under 16, completed daily assessments over seven consecutive days. Participants reported on coparenting cooperation, conflict, and child involvement in conversations, as well as parent-child relationship quality. Actor-Partner Interdependence Models (APIMs) distinguished between stable between-person differences and within-person daily fluctuations. At the between-person level, coparenting cooperation was positively associated with both parents’ own parent–child relationship quality, and child involvement in conversations was positively associated with the partner’s. At the within-person level, daily coparenting conflict predicted lower same-day parent–child relationship quality for mothers, while other same-day and next-day effects were non-significant. These findings highlight the dynamic nature of coparenting effects across temporal levels, emphasizing the distinction between stable relational climate and short-term spillover processes. Practically, interventions may benefit from strengthening long-term cooperative coparenting while helping parents prevent daily conflict from spilling over into parent–child interactions.
Linking Parents’ Appropriated Racial Oppression to Their Racial-Ethnic Socialization Practices: An Intergenerational Examination Among Black Parent-Child Dyads
Journal of Social and Personal Relationships
Shardé McNeil Smith, Anisa Codamon, Chelsea S. Alexander, Jaden Anderson, Shawn C. T. Jones
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Black Americans face not only systemic racism but also the internalization of white supremacist ideologies, a process known as appropriated racial oppression (ARO). ARO involves adopting harmful racial stereotypes that can shape self-concept and behavior. Although its impact on individual well-being is documented, less is known about how ARO influences parenting, particularly racial-ethnic socialization (RES). Therefore, this study explores how parents’ ARO relates to the racial-ethnic socialization messages that their children report receiving, offering additional insight into intergenerational patterns of racial stress. Drawing on the Sociocultural Family Stress Model, we analyzed data from 201 Black parent-adolescent dyads (adolescents: M age = 14.92 years; parents: M age = 43.01 years). We examined six RES dimensions: racial pride, racial barriers, self-worth, egalitarian, negative messages, and socialization behaviors. Higher levels of ARO were associated with fewer positive and more negative RES messages. Skin tone, but not gender, moderated these associations such that the negative effects of ARO on racial barrier, self-worth, and egalitarian messages were stronger among lighter-skinned parents and youth. These findings highlight ARO as a potential barrier to affirming racial socialization practices and underscore the need for culturally grounded interventions that address internalized oppression in Black families.
Solidarity With Friends, Relationship Status, and Mental Health During the COVID-19 Pandemic
Journal of Social and Personal Relationships
Woosang Hwang, Merril Silverstein
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While the solidarity paradigm has been extensively applied to intergenerational relationships, its relevance to adult friendships remains largely unexplored. Therefore, we aimed to uncover distinct types of solidarity with friends among adults and examine whether these types are associated with mental health (depressive symptoms, psychological well-being, and self-esteem) during the COVID-19 pandemic. Furthermore, we examined the moderating role of relationship status (partnered vs. unpartnered) on the above associations. We analyzed 1,353 adults (age range: 17-91) from the Longitudinal Study of Generations (2022) Wave. Latent class analysis with three-step approach was conducted. Four latent classes of solidarity with friends were identified: (1) Tight-knit with many friends , (2) Social with many friends , (3) Functional with few friends , and (4) Detached with few friends . Furthermore, individuals who were identified in the Tight-knit with many friends and Social with many friends latent classes reported better mental health than those in the Detached with few friends latent class. Regarding the moderation effects, we found that unpartnered individuals in the Detached with few friends latent class reported poorer mental health than partnered counterparts, compared to those in Tight-knit with many friends and Social with many friends latent classes. Our findings indicate that solidarity with friends plays a crucial role in promoting mental health in adulthood, especially for those who are unpartnered. By extending the solidarity paradigm to the realm of adult friendships, this study lays a foundation for further inquiry into social networks within family science.
From News Literacy to Skepticism: Examining the Pathways That Shape the “News-Finds-Me” Perception
Journalism & Mass Communication Quarterly
Ying Xiong, Xu Zhang
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Guided by motivated reasoning theory, this study examines how news literacy impacts individuals’ news-finds-me (NFM) perception in the social media environment through a dual-process: identity-motivated skepticism and accuracy-motivated skepticism. Using a structural equation modeling approach with representative samples from the United States, this study’s results revealed that news literacy affected the NFM perception through two distinct skepticism pathways. The findings of this study advance understanding of how news literacy operates through two underlying mechanisms to shape individuals’ NFM perception in an algorithm-based news environment, offering theoretical and practical implications.
Sincere or Strategic: Probing Russian Journalists’ True Convictions Behind Ukraine War Coverage
Journalism & Mass Communication Quarterly
Rashad Mammadov
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How sincere is Russian war journalism? This study investigates whether reporters support Moscow’s Ukraine narrative because they believe it, fear dissent, or already share nationalist convictions. Integrating cultivation theory and spiral of silence logic with a third, ideological pathway, we fielded an encrypted survey of journalists and collected self-submitted articles for frame analysis. Latent class modelling revealed three clusters: internalizers, strategic compliers, and ideological supporters, with texts linked to each class displaying distinctive linguistic signatures that matched survey profiles. By tying motive to discourse, the study offers a transferable template for diagnosing information control and tailoring support for journalists under authoritarian pressure.
Factors Affecting the Adoption of Mobile-Based Digital Technologies: Evidence from Ethiopia
Mobile Media & Communication
Degineh Lagiso Bule, Herbert Ntuli, Colleta Gandidzanwa
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Mobile phones and associated services possess substantial potential to improve household livelihoods. However, there is inadequate access to and use of mobile-based technologies, despite their potential to improve livelihoods in the Hadiya zone, Ethiopia. This study aims to analyse the main factors influencing the adoption of mobile-based technology. The analysis employed an ordered logit model on cross-sectional data collected from 278 respondents. The findings indicate that factors such as gender, education, affordability, access to remittances, social networks, awareness, geographical location, trade access, and type of mobile phone have positively and significantly influenced the likelihood of adopting mobile-based digital technology, except age. The implications suggest that mobile-based technology adoption remains under-utilised for essential activities, with adoption particularly lower among women and rural populations. Targeted interventions, including infrastructure improvements, digital connectivity enhancement, awareness campaigns, skill training, income-raising initiatives, and gender-sensitive digital policies, are essential to promote adoption.
The politics of artificial intelligence alignment: Public reactions to AI moderation in the case of Google’s Gemini
New Media & Society
Adrian Rauchfleisch, Andreas Jungherr
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This study tests how a prominent artificial intelligence (AI) product failure influences public attitudes, focusing on Google Gemini’s generation of controversial images. Drawing on the AI alignment literature, we distinguish three moderation goals that differ in how far they depart from data-driven outputs: safety, bias mitigation, and aspirational imaginaries. We use focusing events research to explain how controversies make governance questions salient. In a preregistered experiment with 1756 participants, we tested responses to two image sets: American Founding Fathers (T1) and German soldiers from 1943 (T2). T1 significantly reduced support for bias-related and aspirational moderation and lowered trust in the company, but did not affect safety-based justifications or perceived political alignment. T2 showed the same directional pattern but did not reach significance; pooled results confirmed the main pattern. These findings show that visible product failures can affect public views on AI governance along dimensions most directly implicated by the controversy.
Mass shooting narratives on social media: Content moderation and participatory discourse
New Media & Society
Huan Chen, Ye Wang, Seth Fallik, Jasmine McNealy, Yuan Nan, Qingyuan Yang, Emily Perpich
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This study examines how mass shooting narratives emerge on social media through the interaction of platform governance and participatory discourse. Using a two-study design, we first compare moderation policies and enforcement approaches across major platforms, then analyze discourse surrounding five mass shooting events on X as visible outcomes of platform governance. For the two highest-volume events, 24,748 X posts from the Chiefs Parade shooting and 2043 X posts from the Hookah Bar shooting, we employ a retrieval-augmented generation (RAG)-based thematic analysis combining human-guided theme development with embedding-based semantic retrieval. Three lower volume events are examined through human thematic analysis, and 267 Reddit posts from the Chiefs Parade case serve as a qualitative comparative reference. Findings show that users shared breaking news, expressed grief, debated gun violence and media coverage, and interpreted events publicly. Platform governance appears associated with differences in discourse visibility, tone, and persistence.
AI meets politics: Examining the effects of different targeting strategies across 15 countries
New Media & Society
Sanne Kruikemeier, Svenja SchÀfer, Alice Hamilton, Puck Guldemond, Jade Vrielink, Carmen Dymanus, Annelien van Remoortere, Sanne Tamboer, Rens Vliegenthart, Susan Vermeer, Sophie C. Boerman
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This study investigates the persuasive effects of messages generated with artificial intelligence in online political targeting, focusing on three distinct strategies: targeting based on political orientation, age, and personality traits. Through an experimental design conducted across 15 countries ( N = 7118), we uncovered that political targeting based on voters’ pre-existing political orientation, receiving messages from a party that is already favored by the receiver, had a persuasive impact on voters. These effects included higher likability of the ad and heightened issue importance. Contrary to popular belief, targeting based on age or a combination of multiple categories does not affect persuasive outcomes. Taken together, by building upon a cross-national analysis, this research provided a robust analysis of how multiple targeting strategies influence the electorate in an EU election context.
Mapping the disinformation industry in Russia
New Media & Society
Serge Poliakoff, Julia Kling
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This article examines disinformation production as a form of media labour and production infrastructure, introducing a novel methodological approach based on large-scale open-source labour data. By systematically analysing the CVs of employees of disinformation production organisations, it demonstrates how CV data can be used to reconstruct organisational structures, technological toolkits, recruitment dynamics, and labour-market embeddedness in contexts where direct fieldwork is constrained or impossible. Drawing on Actor–Network Theory as an analytical lens, the analysis treats CVs as material traces of digital labour infrastructures and organisational alignment. Using case studies of major Russian disinformation organisations, the article shows how workforce dynamics track key political developments, how toolkits reflect shifting organisational priorities, and how disinformation labour has become embedded within state-aligned labour markets. It concludes by outlining the methodological implications of this approach for studying covert or inaccessible organisations and by reflecting on its limitations and further research directions.
Too sure or not sure enough? Trust may hinge on scientists’ uncertainty matching knowledgeable audiences’ tolerance for it
Public Understanding of Science
Natasha Strydhorst, Asheley R. Landrum
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Uncertainty, though integral to scientific practice and advancement, is routinely omitted from science communication. While extensive study has uncovered the effect of this in politicized scientific fields, its influence in unpoliticized sciences is murkier. This survey-experiment study in the USA investigates audience perceptions of communicator trustworthiness when reading excerpts of neuroimaging journalism portraying the field as (a) uncertainty-filled or (b) certain enough to be on the cusp of enabling mind-reading. Findings bolster hitherto mixed results and (exploratorily) suggest perceived communicator trustworthiness may hinge on an author’s uncertainty matching audience members’ tolerance for uncertainty—when they score high in science literacy.
We Are Equally Vulnerable? An Examination of the Third-Person Effect in AI-Generated Misinformation
Social Media + Society
Baoying Fu, Xueqing Li
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Misinformation is a pervasive problem in the social media era, undermining social trust, while the emergence of AI-generated misinformation further exacerbates this issue and threatens social order. This study employs the third-person effect as a theoretical framework and collected 726 questionnaire responses to examine individuals’ perceptions of AI-generated misinformation. When exploring the relationships among misinformation exposure, social undesirability, perceived realism, and AI literacy with respect to perceived effects on oneself and on others, individuals recognize that they themselves are also easily influenced by AI-generated misinformation. Furthermore, perceived effects on oneself and others are positively correlated with corrective actions, while no significant relationship is observed with restrictive actions, thus providing suggestions for individual involvement in combating AI-generated misinformation.
Identity–Policy Fusion and Polarization in United States House Campaign Language
The International Journal of Press/Politics
Tenzin Tamang
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Campaign language in the United States has grown markedly polarized, with candidates increasingly speaking in party-distinctive ways. Yet the mechanisms driving this linguistic divergence remain insufficiently understood. This study proposes identity–policy fusion, a framing strategy in which candidates embed distinctive biographical vocabulary into policy statements, as one factor shaping this divergence. By framing policy as an authentic extension of lived experience, fusion may tie stances to in-group identity and biographical authority, raising the psychological and social costs of disagreement. Using computational text analysis of 41,842 policy statements from 3,343 U.S. House candidates in the 2018, 2020, and 2022 election cycles, the study operationalizes fusion as term frequency–inverse document frequency (TF–IDF)-weighted lexical overlap between biographies and policy texts, and polarization as a statement’s relative similarity to in-party versus out-party linguistic norms within policy domains. Ordinary least squares regression shows that fusion is significantly associated with higher polarization in campaign language, with the association approximately 26 percent stronger for Republicans than Democrats. This partisan asymmetry is consistent with fusion serving as an alternative source of legitimation under conditions of contested institutional authority, illuminating a potential mechanism through which elite messaging may harden partisan boundaries.
Risky appeals: The electoral consequences of group-targeted campaign pledges
European Journal of Political Research
Isabelle Guinaudeau, Elisa Deiss-Helbig, Theres Matthieß
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Parties frequently target campaign promises on specific social groups, yet we lack evidence on whether such targeting yields greater electoral pay-offs than broad-based universalistic pledges. We address this gap with a pre-registered survey experiment fielded in Germany in 2024 ( N = 3,500). We expose respondents to a fictional electoral campaign scenario featuring posters promising additional public spending either to the general population (broad-based pledge), or to a specific group – parents, pensioners, or rural residents (group-targeted pledge) – and examine how voters respond. We theorize that group targeting should raise the salience of party-group linkages and therefore boost support among voters who (1) belong to the target group, (2) identify with this group, and/or (3) view it as deserving. At the same time, it may alienate others who perceive such pledges as unfair. We find no consistent evidence that group-targeted pledges outperform broad-based ones in generating electoral support – even among intended beneficiaries. Instead, responses to targeted appeals are strongly moderated by group belonging and perception: support remains stable or slightly lower among intended beneficiaries, but drops substantially among other respondents. These patterns suggest that rather than securing net electoral gains, group-targeted promises can provoke exclusion-driven losses that outweigh limited ingroup appeal. More broadly, the study highlights how identity and deservingness perceptions shape voter reactions to realistic campaign pledges – and how even appeals to normatively ‘deserving’ or majoritarian groups may risk narrowing rather than broadening electoral support.
‘Get the shot, or else!’ Policy coercion and institutional trust are compensatory for vaccine uptake
European Journal of Political Research
Alexandru D. Moise, Evelyne HĂŒbscher
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What are the determinants of individual uptake of vaccination? Using original data from a survey fielded in September 2021 in Germany and the United Kingdom, this study looks at the impact of three factors on individual vaccination during the COVID-19 pandemic. In a first study using observational data, we look at individual trust in institutions and political ideology. In a second study, based on experimental data, we assess the impact restrictions for unvaccinated individuals in the form of ‘green pass’ policies have on the propensity to get vaccinated. Results from the first study show that trust in institutions and ideology are associated with vaccination uptake. Results from the survey experiment indicate that the ‘green pass’ policy scenario significantly increased willingness to get a booster shot for Germans, but not for UK respondents, due to a ceiling effect in the United Kingdom. We further ask whether the effects of trust and policy coercion ‘amplify’ or ‘compensate’ each other. We find that trust has a ‘compensation’ effect, whereby individuals not yet vaccinated are considerably more likely to do so if they trust political institutions. Trust also compensates for other policy measures, as trusting individuals are highly likely to get vaccinated with or without the ‘green pass’ policy incentive, whereas low-trust individuals are more likely under the ‘green pass’ scenario.
A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies
Journal of Experimental Political Science
Zhongren Chen, Joshua Kalla, Quan Le, Shinpei Nakamura-Sakai, Jasjeet Sekhon, Ruixiao Wang
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We investigate whether large language models (LLMs) threaten democracy through their persuasive capabilities. Using two survey experiments ( N = 10,417) and real-world simulations, we compare the cost-effectiveness of LLM chatbots against traditional campaign tactics, taking into account both the “receive” and “accept” steps in the persuasion process. Our design advances prior research by assessing extended human-LLM interactions and measuring short- and long-term effects across three political domains. We find that while LLMs are comparably persuasive to campaign ads once seen, real-world impact depends on both message reception and acceptance. Simulations estimate LLM-based persuasion costs $48–$75 per voter versus $100 for traditional methods. However, traditional methods currently scale more effectively. While LLMs do not yet offer substantially greater potential for large-scale persuasion, this may shift as capabilities improve and techniques for scalable exposure become feasible.
Post-Treatment Problems: What Can We Say about the Effect of a Treatment among Sub-Groups Who (Would) Respond in Some Way?
Political Analysis
Chad Hazlett, Nina McMurry, Tanvi Shinkre
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Investigators are often interested in how a treatment affects an outcome for units responding to treatment in a certain way. We may wish to know the effect among units that, for example, meaningfully implemented an intervention, passed an attention check or demonstrated some important mechanistic response. Simply conditioning on the observed value of the post-treatment variable introduces problematic biases. Further, the identification assumptions required by several existing strategies are often indefensible. We propose the treatment reactive average causal effect (TRACE), which we define as the total effect of treatment in the group that, if treated, would realize a particular value of the relevant post-treatment variable. By reasoning about the effect among the “non-reactive” group, we can identify and estimate the range of plausible values for the TRACE. We demonstrate the use of this approach with three examples: (i) learning the effect of police-perceived race on police violence during traffic stops, a case where point identification may be possible; (ii) estimating effects of a community policing intervention in Liberia, in communities that meaningfully implemented it; and (iii) studying how in-person canvassing affects support for transgender rights, among participants for whom the intervention would result in more positive feelings toward transgender people.
Prediction-Powered Estimation: Unbiased Model-Assisted Estimation
Journal of Official Statistics
Nicholas Denis, Mohammed Haddou
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National statistical agencies increasingly face budget constraints and shrinking sample sizes, while simultaneously gaining access to rich auxiliary data and powerful pre-trained machine learning (ML) and artificial intelligence (AI) models, including Large Language Models (LLMs). Traditional model-assisted estimation techniques, which fit models using survey sample data, are limited by small sample sizes, struggle to leverage complex non-linear relationships in auxiliary data, and cannot accommodate frontier pre-trained models. This work re-examines the use of pre-trained black-box models, fit independently of the survey sample, for design-based parameter estimation. Inspired by the Prediction-Powered Inference (PPI) framework, we introduce the Prediction-Powered Estimator (PPE), an unbiased estimator with an unbiased variance estimator for the survey design setting. We also formalize the use of pre-trained models with the classic difference estimator—which we term the Prediction-Powered Difference (PPD) estimator—and with the Generalized Regression Estimator via predicted values as covariates ( GREG y ^ ). Through LLM-based use-cases leveraging unstructured auxiliary data (images and text) and experiments with real-world survey data from Statistics Canada, complemented by simulation studies in the Supplemental Material , we demonstrate that these approaches consistently outperform standard baseline estimators across bias, mean absolute error, mean squared error, coverage, and confidence interval width. The results suggest that pre-trained models can yield more accurate and efficient estimates while potentially reducing survey sample sizes and respondent burden, and motivate expanding the survey methodologist’s toolbox to include pre-trained models and novel auxiliary data sources.
EXPLORING THE POTENTIAL OF NOVEL PARADATA IN RESPONDENT-DRIVEN SAMPLING
Journal of Survey Statistics and Methodology
Sunghee Lee, Leng Seong Che
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This study extends the scope of the paradata discussion to respondent-driven sampling (RDS). Unlike traditional sampling, RDS relies on existing social networks within a target population. This unique process provides opportunities to produce novel paradata. Specifically, this study examined two types of paradata in RDS: one based on interviewer observations and the other based on recruitment behaviors ascertained from tracking recruitment coupons. We implemented these paradata features in two independent RDS surveys. In an in-person RDS survey of persons who inject drugs in Southeast Michigan, we implemented an interviewer observation questionnaire. This included questions about interviewers’ assessments of respondents’ understanding of coupon distribution instructions, as well as their expectations regarding respondents’ chances to recruit others and to return for a follow-up interview. These observations predicted recruitment success. In a Web-RDS study of Korean Americans, physical distance between linked respondents (such as a respondent and their recruiter) was determined by tracking recruitment coupons and geocoding respondent addresses. Greater geographic distance was associated with a higher likelihood of serious psychological distress. The results demonstrate that the unique features of RDS offer new avenues for utilizing paradata in both methodological and substantive research. These findings warrant further exploration and development of paradata specific to RDS.
Whose Centre Holds? White Normativity in Race Dimensions Across Word Embeddings
Social Science Computer Review
Nnaemeka Ohamadike, Kevin Durrheim, Mpho Primus
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Bias in word embeddings is often measured using bipolar dimensions, constructed as the difference between two anchor centroids. This technique assumes both poles are symmetrical and equally informative. However, normativity literature shows that one category may function as the unmarked norm, with others framed as marked deviations. In race, whiteness typically holds the normative position, and embedding-based race dimensions may inherit the skew. We test this possibility using dimensions constructed from validated African–European name anchors, probed with neutral and valence words. In three embedding models (Wiki-News, South African news, Google News), we assess whether race dimensions favour whiteness as a normative anchor, whether this skew is stronger in culturally specific models (SA, Google), and whether bipolar offsets amplify one pole, given unipolar evidence. Results show that neutral and valence terms cluster nearer to the white pole (most strongly in the Wiki-News model), indicating whiteness as the semantic default. Overshoot favoured Black in Google and Wiki-News, while White overshoot only occurred in the South African model. We argue that this captures racialised variance where the pole with more spread tends to exert greater leverage on the bipolar axis. The study provides quantitative evidence of white-normative anchoring and diagnostics for asymmetric amplification in embedding-based bias measures.
A Multimethod Assessment of Spontaneous Behavioral Synchrony in Race- and Age-Concordant Versus Discordant Dyads
Personality and Social Psychology Bulletin
Morgan D. Stosic, Adele E. Weaver, Ken Fujiwara, Ishabel M. Vicaria, Derek M. Isaacowitz, Mollie A. Ruben
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This work examines the impact of race and age concordance on behavioral synchrony and self-reported rapport in social interactions, employing a multimethod approach across three studies ( N total = 450) and a mini meta-analysis. Behavioral synchrony was assessed with reliable human coders and OpenPose, and rapport was measured through self-reports. Results indicated that race- and age-concordant dyads exhibited significantly greater behavioral synchrony than discordant dyads, suggesting that shared visible social identities may be associated with smoother, more connected interactions. Synchrony effects were stronger when measured by human coders compared to OpenPose, potentially reflecting human coders’ biases related to perceived similarity between interactants or coders’ more nuanced ability to detect certain components of synchrony compared to technological approaches. No significant effects of race or age concordance on self-reported rapport were observed. These findings highlight an important factor in predicting the spontaneous emergence of behavioral synchrony and emphasize the value of integrating multimethod approaches.
Discovering Preference Structure Using Randomized Paired Comparisons in Surveys: A Topic Modeling Approach
Sociological Methods & Research
Jeong-han Kang, Eunrang Kwon, Junmo Song
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Randomized paired comparisons (RPC) for social values have various advantages over a matrix format of multiple items; however, their use cannot exhaust all possible pairs if there are too many items to compare one-to-one. This article proposes (1) applying a dimension reduction method, structural topic modeling (STM), to RPC survey data by restructuring answers into ordered pairs to estimate latent answering patterns, (2) visualizing them into directed graphs, and (3) interpreting them as respondents’ preference structures among social values. For empirical validation, we randomly divided 920 respondents into RPC and matrix-format groups and asked about the seriousness of ten social problems. Our STM from the RPC group revealed five preference structures beyond a linear order among the 10 items, which are interpretable and incorporate statistical tests with respondents’ traits as covariates. We also discuss how to improve topic modeling with RPC and contribute to various research streams, such as cultural value networks and gamification, by pairwise wiki survey.
Homo cooperans : Understanding the nature of human cooperation
Science
Peter Andre, Teodora Boneva, Felix Chopra, Armin Falk
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Human cooperation is fundamental to solving collective challenges, yet its individual drivers remain insufficiently understood. Using globally representative data from an incentivized two-player cooperation experiment conducted in 125 countries ( N = 101,123 individuals), we assess the extent of human cooperation and study its individual determinants. Across the globe, about two-thirds of people choose to cooperate. The decision to cooperate is significantly shaped by cooperation beliefs, injunctive norms, and preferences. Effect sizes of these determinants vary across countries, a variation that is systematically associated with historical and cultural markers. Globally, people underestimate others’ willingness to cooperate—humans are more cooperative than they believe. A simple information treatment reduces misperceptions and causally increases cooperation.