Social Science: Research Area — Prolific Citations Library

Discover 49 peer-reviewed studies in Social Science (2025–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 49 peer-reviewed papers in the research area of Social Science in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 49)

Cognitive empathy and dehumanization co-vary with dark triad traits and agency detection sensitivity

Authors: K Rudnicki, O Borowiecki, K Poels, B Beersma

Year: 2026

Published in: Evolution and Human …, 2026 - Elsevier

Institution: University of Antwerp, University of Bialystok, VU University, Emory University

Research Area: Personality psychology, Social cognition, Cognitive neuroscience

Discipline: Evolutionary psychology, human behavioral ecology

In a preregistered study, psychopathy (more than the other Dark Triad traits) is linked to worse cognitive empathy and greater dehumanization, and this empathy–psychopathy link is especially strong among people who are less sensitive at detecting agency in others.
Moral Lenses, Political Coordinates: Towards Ideological Positioning of Morally Conditioned LLMs

Authors: C Yuan, B Ma, Z Zhang, B Prenkaj, F Kreuter, G Kasneci

Year: 2026

Published in: arXiv preprint arXiv:2601.08634, 2026•arxiv.org

Institution: Munich Center for Machine Learning, LMU Munich, Technical University of Munich

Research Area: Artificial Intelligence, AI Ethics, AI Alignment, Political Science, Computational Social Science

Discipline: Computer Science, Natural Language Processing (NLP)

This paper examines how large language models’ (LLMs) political outputs shift when you explicitly prime them with different moral values. Instead of just assigning fake personas (like “pretend to be liberal”), the authors condition models to endorse or reject specific moral values (e.g., utilitarianism, fairness, authority). They then measure how those moral primes move the models’ positions in...

DOI: https://doi.org/10.48550/arXiv.2601.08634
Toward Measuring Geographical Belongingness in a Mobile, Hybrid Globe – Development of a Place-Belongingness Scale Among Immigrants

Authors: H Mohseni, T Kujala, J Silvennoinen

Year: 2026

Published in: SPRINGER

Institution: University of Jyväskylä

Research Area: Migration studies, Social indicators, Psychometrics, Quantitative social science methods

Discipline: Social sciences

Developed and validated a multidimensional place-belongingness scale to assess immigrants' sense of belonging to geographic locations, identifying four factors: feeling at home, accepted, empowered, and secure.

Methods: Survey data from 270 immigrants worldwide analyzed using exploratory factor analysis.

Key Findings: The subjective sense of place-belongingness, decomposed into four factors: feeling at home, feeling accepted, feeling empowered, and feeling secure.

Sample Size: 270
What large language models know and what people think they know

Authors: M Steyvers, H Tejeda, A Kumar, C Belem

Year: 2025

Published in: Nature Machine ..., 2025 - nature.com

Institution: University of California Irvine

Research Area: Computational Linguistics, Computational Social Science, AI Ethics, Trust in AI

Discipline: Computational Social Science

LLMs often lead to user overestimation of response accuracy, especially with longer explanations; adjusting explanation styles to align with model confidence improves calibration and discrimination gaps, enhancing trust in AI-assisted decision making.

Methods: Conducted experiments using multiple-choice and short-answer questions to study user confidence versus model-stated confidence; varied explanation length and alignment with model internal confidence.

Key Findings: Calibration gap (human vs. model confidence), discrimination gap (ability to distinguish correct vs. incorrect answers), and effects of explanation style and length on user trust.

Citations: 100
Impact of annotator demographics on sentiment dataset labeling

Authors: Y Ding, J You, TK Machulla, J Jacobs, P Sen

Year: 2025

Published in: Proceedings of the ..., 2022 - dl.acm.org

Institution: University of California Irvine, University of Florida, State University of New York at Buffalo, University of Waterloo, Virginia Tech

Research Area: Computational Social Science, Human-Computer Interaction (HCI), Sentiment Analysis

Discipline: Computational Social Science

Demographic differences among annotators significantly affect sentiment dataset labels, causing up to a 4.5% accuracy difference in sentiment prediction models.

Methods: Crowdsourced annotations from >1000 workers combined with demographic data; analysis of multimodal sentiment datasets and evaluation using machine learning models.

Key Findings: Impact of annotator demographics on sentiment labeling and its effect on model predictions.

DOI: https://doi.org/10.1145/3555632

Citations: 28

Sample Size: 1000
Tokenization of social media engagements increases the sharing of false (and other) news but penalization moderates it

Authors: M Alizadeh, E Hoes, F Gilardi

Year: 2025

Published in: Scientific Reports, 2023 - nature.com

Institution: Department of Marketing, University of Amsterdam, Department of Social Sciences, Università Degli Studi di Milano, Department of Political Science and International Relations, Università Degli Studi di Milano

Research Area: Social media, Misinformation, Computational Social Science

Discipline: Computational Social Science

Token-based incentives for social media engagement increase the sharing of misinformation, but implementing penalties for objectionable content can reduce this trend without fully eliminating it.

Methods: Survey experiment analyzing the impact of hypothetical token rewards and penalties on user willingness to share different types of news content.

Key Findings: Effect of token-based incentives and penalties on user engagement and the willingness to share misinformation.

DOI: https://doi.org/10.1038/s41598-023-40716-2

Citations: 20
Engaging with (vs. avoiding) personalized advertising on social media

Authors: SMC Loureiro, L Hollebeek, RA Rather

Year: 2025

Published in: Journal of Marketing ..., 2025 - Taylor & Francis

Institution: Universitário de Lisboa

Research Area: Marketing Communications, Social Media, Behavioral Science

Discipline: Marketing, Behavioral Science

Personalized advertising on social media enhances brand engagement and alleviates privacy concerns, with privacy concerns having no significant effect on consumer-brand engagement.

Methods: Grounded in social exchange theory, the study utilized a quantitative survey to assess relationships between personalized advertising, information control, privacy concerns, advertising avoidance, and brand engagement.

Key Findings: The interplay between personalized advertising, consumer brand engagement, privacy concerns, information control, and advertising avoidance.

Citations: 17

Sample Size: 429
A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies

Authors: Z Chen, J Kalla, Q Le, S Nakamura-Sakai

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: The affiliated institutions could not be determined from the provided context or an external search of the URL.

Research Area: Artificial Intelligence and Social Science, Persuasion Studies, Political Persuasion, LLM Chatbots, Democratic Societies

Discipline: Artificial Intelligence, Social Science

The study evaluates the cost-effectiveness and persuasive risks of Large Language Model (LLM) chatbots in political contexts, finding that while LLMs are as persuasive as campaign ads under exposure, their large-scale influence is currently limited by scalability and cost barriers.

Methods: Two survey experiments combined with real-world simulation exercises to measure the persuasiveness of LLM chatbots compared to traditional campaign tactics, focusing on both exposure and acceptance phases of persuasion.

Key Findings: Short- and long-term persuasive effects of LLMs, cost-effectiveness of LLM-based persuasion ($48-$74 per persuaded voter), and scalability compared to traditional campaign approaches.

Citations: 7

Sample Size: 10417
The Daily Lives of Crowdsourced US Respondents: A Time Use Comparison of MTurk, Prolific, and ATUS

Authors: RG Rinderknecht, L Doan

Year: 2025

Published in: Sociological ..., 2025 - journals.sagepub.com

Institution: RAND

Research Area: Crowdsourcing Research Methods, Time Use Studies, Social Science

Discipline: Artificial Intelligence

Time use patterns of MTurk and Prolific respondents differ significantly from the general U.S. population (ATUS), including less housework and care work, more time at home and alone, even after accounting for demographic differences.

Methods: Time diaries were collected and analyzed for 136 MTurk and 156 Prolific respondents, then compared with 468 ATUS responses.

Key Findings: Daily time use patterns including work, housework, travel, leisure, and time spent alone or at home.

Citations: 6

Sample Size: 760
Age and gender distortion in online media and large language models

Authors: D Guilbeault, S Delecourt, BS Desikan

Year: 2025

Published in: Nature, 2025 - nature.com

Institution: Stanford University, University of California Berkeley, University of Oxford

Research Area: AI Bias, Media Representation, Social Science

Discipline: Computational Social Science, Artificial Intelligence

The study highlights age-related gender bias in online media and language models, showing women are portrayed as younger than men, especially in high-status occupations, and explores how algorithms amplify these biases.

Methods: Analysis of 1.4 million images and videos from online sources and nine language models, followed by a pre-registered experiment involving participants to evaluate biases in internet content and algorithms.

Key Findings: Age and gender bias in occupational depiction across online platforms and language models, as well as its influence on beliefs and hiring preferences.

Citations: 4

Sample Size: 459
Understanding the Role of Social Media Content in Inducing Anxiety: A Qualitative Study

Authors: A Meythaler

Year: 2025

Published in: 2025 - scholarspace.manoa.hawaii.edu

Institution: University of Potsdam, Weizenbaum Institute

Research Area: Social Media, Anxiety, Qualitative Research, Computational Social Science

Discipline: Psychological Science, Computational Social Science

The study identifies six categories of social media content—negative news, incivility, social comparison content, political content, misinformation, and depictions of dangerous behavior—as triggers for anxiety among users.

Methods: A qualitative study was conducted using interviews or focus groups with 249 social media users to explore the effects of different content types on anxiety.

Key Findings: The role of specific social media content categories in inducing feelings of anxiety.

DOI: https://doi.org/10.24251/HICSS.2025.334

Citations: 4

Sample Size: 249
An experimental online study on the impact of negative social media comments on anxiety and mood

Authors: Y Ai, A von Mühlenen

Year: 2025

Published in: Scientific Reports, 2025 - nature.com

Institution: University of Warwick

Research Area: Social media, Mental Health, Behavioral Science

Discipline: Behavioral Science

Negative social media comments significantly increase anxiety and decrease mood, with younger adults showing heightened sensitivity compared to older adults.

Methods: Participants shared blog posts on a simulated internet forum and were exposed to negative, neutral, or positive comments; mood and anxiety levels were measured using validated scales.

Key Findings: Impact of negative, neutral, and positive social media comments on anxiety and mood across adult participants.

Citations: 3

Sample Size: 128
ACM Journal on Responsible Computing | Volume 2, Number 4

Authors: J van Grunsven, N Jacobs, BA Kamphorst, M Honauer

Year: 2025

Published in: ACM Journal on, 2025 - dl.acm.org

Institution: University of Texas, Microsoft Research, Google DeepMind, Google, University of Washington, World Economic Forum

Research Area: Ethics and Governance of Computing Research, focused on Responsible Computing, Social Science Research, Artificial Intelligence.

Discipline: Ethics, Governance of Computing Research, AI Ethics

The paper emphasizes the importance of accounting for human vulnerability in the design and analysis of digital technologies, proposing concepts like 'Intimate Computing' to empower individuals in managing their technology-mediated vulnerabilities.

Methods: The study reviews and synthesizes existing literature and frameworks addressing vulnerability in human-technology interactions, including concepts like 'Intimate Computing' and 'Person-Machine Teaming'.

Key Findings: Human vulnerability in the context of digitally-mediated interactions and the role of computing frameworks in addressing them.

Citations: 2
iNews: A multimodal dataset for modeling personalized affective responses to news

Authors: T Hu, N Collier

Year: 2025

Published in: arXiv preprint arXiv:2503.03335, 2025 - arxiv.org

Institution: University of Cambridge

Research Area: Affective Computing, Natural Language Processing, Computational Social Science

Discipline: Computational Social Science

The iNews dataset is a multimodal resource for studying personalized affective responses to news, improving modeling accuracy by incorporating annotator persona metadata.

Methods: 292 demographically diverse UK participants annotated 2,899 Facebook news posts with multidimensional labels (e.g., emotions, valence, arousal), combined with comprehensive participant persona data.

Key Findings: Modeled personalized affective responses to news through annotations capturing valence, arousal, emotions, and persona metadata.

Citations: 2

Sample Size: 2899
Crowdsourcing: a modern tool for robust research sampling

Authors: JS Michel, G Sawhney, GP Watson

Year: 2025

Published in: How to Conduct and ..., 2025 - elgaronline.com

Institution: Auburn University

Research Area: Crowdsourcing, Research Methods, Social Science

Discipline: Social Science

Crowdsourcing is a versatile tool leveraging collective intelligence for efficient task completion and has applications across various fields including decentralized finance, blockchain technologies, and IO Psychology research and practice.

Methods: The paper discusses the theoretical and practical applications of crowdsourcing in various domains, referencing prior work and examples such as Wikipedia, crowdfunding platforms, and blockchain networks.

Key Findings: The applications and impact of crowdsourcing in different fields, particularly its role in Industrial-Organizational Psychology for data collection and analysis.

Citations: 1
Evaluating LLM-contaminated Crowdsourcing Data Without Ground Truth

Authors: Y Zhang, J Pang, Z Zhu, Y Liu

Year: 2025

Published in: arXiv preprint arXiv:2506.06991, 2025 - arxiv.org

Institution: Rutgers University, University of California Santa Cruz

Research Area: Artificial Intelligence, Computational Social Science

Discipline: Computational Social Science

The paper proposes a training-free scoring mechanism using peer prediction to detect and mitigate LLM-assisted cheating in crowdsourced annotation tasks, with theoretical guarantees and empirical validation.

Methods: A peer prediction-based mechanism quantifies correlations between worker answers while conditioning on LLM-generated labels, without requiring ground truth or high-dimensional training data.

Key Findings: Detection of LLM-assisted low-effort cheating in crowdsourced annotation tasks, focusing on theoretical effectiveness and empirical robustness.

DOI: https://doi.org/10.48550/arXiv.2506.06991

Citations: 1
Useful or Creepy? The Effect of Exchanged Benefits and Information Collection in Social Media Advertisements

Authors: S Kwon, NL Kim

Year: 2025

Published in: International Textile and Apparel ..., 2025 - iastatedigitalpress.com

Institution: University of Minnesota

Research Area: Social Media Advertising, Consumer Perception, Information Collection Ethics in Marketing, Social Science.

Discipline: Social Science, Marketing

Consumers are more willing to disclose personal information in social media advertising when they perceive exchanged benefits, such as monetary rewards and personalized recommendations, outweigh the risks; the method of information collection (overt vs. covert) does not significantly affect this decision.

Methods: An online survey was conducted among U.S. Instagram users to assess attitudes toward benefit-risk trade-offs in personal data disclosure for advertising purposes.

Key Findings: Willingness to disclose personal information, click-through intentions, and purchase intentions based on perceived benefits and risks in social media advertisements.

DOI: https://doi.org/10.31274/itaa.18830

Citations: 1

Sample Size: 199
Using artificial intelligence to generate visual vignettes in factorial survey experiments

Authors: N Schwitter

Year: 2025

Published in: Social Science Computer Review, 2025 - journals.sagepub.com

Institution: University of Lucerne

Research Area: Artificial Intelligence in Social Science Research Methods, Factorial Survey Experiments, Visual Vignettes Generation

Discipline: Social Science

This paper explores the use of generative AI for creating visual vignettes in factorial survey experiments, highlighting their potential to boost realism and engagement while addressing ethical and technical challenges.

Methods: Techniques for generating and selectively editing AI-generated images were demonstrated, and a pretest with human participants was conducted to evaluate perceptions and interpretations of the images.

Key Findings: Application of AI-generated visual vignettes in social science research and participant interpretation of these images.

Citations: 1
Algorithmic surveillance and workers' compliance: The role of trust, privacy concerns, and fairness in online crowdwork

Authors: W van Zoonen, ME von Bonsdorff

Year: 2025

Published in: human ..., 2025 - journals.sagepub.com

Institution: Wageningen University & Research, University of Twente

Research Area: Organizational Behavior, Human Resources, or Social Science focusing on Technology and Ethics in the Workplace.

Discipline: Social Science

The study shows that algorithmic surveillance undermines trust and fairness, while increasing privacy concerns among crowdworkers, influencing their compliance, alteration, or resistance behaviors, with decontextualization intensifying these dynamics.

Methods: Three-wave survey data analysis of European online crowdworkers, analyzed through socio-technical systems theory and micro-level legitimacy frameworks.

Key Findings: The effects of algorithmic surveillance on trust, privacy concerns, fairness, and workers' compliance, alteration, or resistance, with a focus on the moderating role of perceived decontextualization.

Sample Size: 435
Biased By Design: Leveraging AI Biases to Enhance Critical Thinking of News Readers

Authors: Liudmila Zavolokina, Kilian Sprenkamp, Zoya Katashinskaya, Daniel Gordon Jones

Year: 2025

Published in: ArXiv

Institution: University of Zurich

Research Area: AI Ethics, AI Bias, News Literacy, Critical Thinking, Computational Social Science

Discipline: Computational Social Science

The study explores leveraging inherent biases in AI to enhance critical thinking in news consumption, proposing strategies such as bias awareness, personalization, and gradual introduction of diverse perspectives.

Methods: Qualitative user study investigating user responses to personalized AI-driven propaganda detection tools.

Key Findings: The effectiveness of AI bias-based strategies in improving critical thinking and news readers’ engagement with diverse perspectives.