Social: Academic Discipline — Prolific Citations Library

Explore 40 peer-reviewed papers in Social (2025–2026). Academic research using Prolific for high-quality human data collection.

This page lists 40 peer-reviewed papers in the discipline of Social in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (20 of 40)

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
Toxic content and user engagement on social media: Evidence from a field experiment

Authors: G Beknazar-Yuzbashev, R Jiménez-Durán, J McCrosky

Year: 2025

Published in: 2025 - econstor.eu

Institution: Mozilla Foundation, Columbia University, Bocconi University, Stanford University, University of Warwick

Research Area: Social Media, User Engagement, Toxicity

Discipline: Social Science

Reducing exposure to toxic content on social media lowers user engagement but also decreases the toxicity of user-generated content, highlighting a trade-off for platforms between reduced toxicity and increased engagement.

Methods: Pre-registered browser extension field experiment on Facebook, Twitter, and YouTube to randomly hide toxic content for six weeks; supplemented with a survey experiment.

Key Findings: Impact of reduced exposure to toxic content on advertising impressions, time spent, engagement, and user-generated content toxicity; explored curiosity and alignment between engagement and welfare.

Citations: 76
Human detection of political speech deepfakes across transcripts, audio, and video

Authors: M Groh, A Sankaranarayanan, N Singh, DY Kim

Year: 2025

Published in: Nature ..., 2024 - nature.com

Institution: Northwestern University, Massachusetts Institute of Technology

Research Area: Deepfakes, Media Forensics, Human Perception of AI-Generated Content, Political Communication

Discipline: Computational Social Science

Humans are better at detecting deepfake political speeches using audio-visual cues than relying on text alone; state-of-the-art text-to-speech audio makes deepfakes harder to discern.

Methods: Five pre-registered randomized experiments with varied base rates of misinformation, audio sources, question framings, and media modalities were conducted.

Key Findings: Human accuracy in discerning real political speeches from deepfakes across media formats and contextual variables.

DOI: https://doi.org/10.1038/s41467-024-51998-z

Citations: 63

Sample Size: 2215
Dimensions of diversity in human perceptions of algorithmic fairness

Authors: N Grgić-Hlača, G Lima, A Weller

Year: 2025

Published in: Proceedings of the 2nd ..., 2022 - dl.acm.org

Institution: Max Planck Institute, École Polytechnique Fédérale de Lausanne, University of Cambridge, The Alan Turing Institute

Research Area: Algorithmic Fairness, Human Perception, Diversity in AI Decision-Making

Discipline: Social Science, Artificial Intelligence

This study examines how sociodemographic factors and personal experience influence perceptions of fairness in algorithmic decision-making, particularly in bail decisions, highlighting the importance of diverse perspectives in regulatory oversight.

Methods: Explored perceptions of procedural fairness using surveys to assess the influence of demographics and personal experiences.

Key Findings: Impact of demographics (age, education, gender, race, political views) and personal experience on perceptions of fairness of algorithmic feature use in bail decisions.

DOI: 10.1145/3551624.3555306

Citations: 62
LLM-generated messages can persuade humans on policy issues

Authors: H Bai, JG Voelkel, S Muldowney, JC Eichstaedt

Year: 2025

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

Institution: Stanford University

Research Area: Political Persuasion, LLM

Discipline: Computational Social Science

LLM-generated messages can effectively persuade humans on policy issues similarly to human-crafted messages, with differences in perceived persuasion mechanisms.

Methods: Three pre-registered experiments were conducted comparing the persuasive effectiveness of LLM-generated and human-generated messages on policy attitudes, using control conditions with neutral messages.

Key Findings: Influence of LLM-generated messages on participants' policy attitudes and perceived characteristics of the message authors.

Citations: 37

Sample Size: 4829
Can large language models assess personality from asynchronous video interviews? A comprehensive evaluation of validity, reliability, fairness, and rating patterns

Authors: T Zhang, A Koutsoumpis, JK Oostrom

Year: 2025

Published in: IEEE Transactions ..., 2024 - ieeexplore.ieee.org

Institution: Southeast University, Vrije Universiteit, Tilburg University

Research Area: LLM Personality Assessment, Human-AI Interaction, LLM

Discipline: Human-AI Interaction, Social Science, Humanities

LLMs like GPT-3.5 and GPT-4 can rival or outperform task-specific AI models in assessing personality traits from asynchronous video interviews, but show uneven performance, low reliability, and potential biases, warranting cautious use in high-stakes scenarios.

Methods: The study evaluated GPT-3.5 and GPT-4 performance in assessing personality traits and interview performance using simulated AVI responses, comparing them with ratings from task-specific AI and human annotators.

Key Findings: Validity, reliability, fairness, and rating patterns of LLMs (GPT-3.5 and GPT-4) in personality assessment from asynchronous video interviews.

Citations: 31

Sample Size: 685
Scaling language model size yields diminishing returns for single-message political persuasion

Authors: K Hackenburg, BM Tappin, P Röttger, SA Hale

Year: 2025

Published in: Proceedings of the ..., 2025 - pnas.org

Institution: University of California Berkeley, University of Cambridge, University of Oxford, Max Planck Institute

Research Area: Political Persuasion, LLM

Discipline: Computational Social Science, Political Science

Scaling language model sizes leads to diminishing returns in generating persuasive political messages, with larger models providing minimal gains compared to smaller ones after controlling for task completion metrics like coherence and relevance.

Methods: Generated 720 political messages using 24 LLMs of varying sizes and tested their persuasiveness through a large-scale randomized survey experiment.

Key Findings: Persuasive capability of language models across different sizes in generating political messages.

Citations: 31

Sample Size: 25982
Consumer engagement with brands' COVID-19 messaging on social media: the role of perceived brand-social issue fit and brand opportunism

Authors: J Mundel, J Yang

Year: 2025

Published in: Journal of Interactive Advertising, 2021 - Taylor & Francis

Institution: Arizona State University, Loyola University

Research Area: Consumer Behavior, Social Media Marketing, COVID-19 Studies

Discipline: Marketing, Social Sciences

Brands with strong perceived fit between their products and COVID-19 messaging showed higher consumer engagement and positive attitudes, while those with lower perceived fit faced negative evaluations due to perceptions of opportunism.

Methods: Analyzed consumer responses to Instagram ads using perceived brand-social issue fit as a determinant of ad evaluations, brand attitudes, and engagement intentions.

Key Findings: Consumer responses, ad evaluations, brand attitudes, engagement intentions, and perceived brand opportunism based on fit between product type and COVID-19 messaging.

DOI: https://www.tandfonline.com/doi/abs/10.1080/15252019.2021.1958274#

Citations: 29
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
Generative AI meets open-ended survey responses: Research participant use of ai and homogenization

Authors: S Zhang, J Xu, AJ Alvero

Year: 2025

Published in: Sociological Methods & Research, 2025 - journals.sagepub.com

Institution: University of Maryland, Indiana University, University of Minnesota Duluth

Research Area: Sociological Methods, Generative AI, Survey Methodology

Discipline: Sociology, Social Science

The study finds that 34% of research participants use generative AI tools like large language models (LLMs) to assist with open-ended survey responses, leading to more homogeneity and positivity in their answers, which could impact data validity by masking social variations.

Methods: The study conducted an original survey on a popular online platform and simulated comparisons between human-written responses from pre-ChatGPT studies and LLM-generated responses.

Key Findings: Use of LLMs by survey participants, differences in text homogeneity, positivity, and masking of social variation in open-ended survey responses.

Citations: 26
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
Large Language Models Are More Persuasive Than Incentivized Human Persuaders

Authors: P. Schoenegger, F. Salvi, J. Liu, X. Nan, R. Debnath, B. Fasolo, E. Leivada, G. Recchia, F. Günther, A. Zarifhonarvar, J. Kwon, Z. Ul Islam, M. Dehnert, D. Y. H. Lee, M. G. Reinecke, D. G. Kamper, M. Kobaş, A. Sandford, J. Kgomo, L. Hewitt, S. Kapoor, K. Oktar, E. E. Kucuk, B. Feng, C. R. Jones, I. Gainsburg, S. Olschewski, N. Heinzelmann, F. Cruz, B. M. Tappin, T. Ma, P. S. Park, R. Onyonka, A. Hjorth, P. Slattery, Q. Zeng, L. Finke, I. Grossmann, A. Salatiello, E. Karger

Year: 2025

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

Institution: London School of Economics and Political Science, University of Cambridge, University College London, Massachusetts Institute of Technology, University of Oxford, Modulo Research, Stanford University, Federal Reserve Bank of Chicago, ETH Zürich, University of Johannesburg

Research Area: Computation and Language

Discipline: Social Science, Artificial Intelligence

This paper compares a frontier LLM (Claude Sonnet 3.5) against incentivized human persuaders in a conversational quiz setting, finding that the AI's persuasion capabilities surpass those of humans with real-money bonuses tied to performance.

Citations: 16
The levers of political persuasion with conversational artificial intelligence

Authors: K Hackenburg, BM Tappin, L Hewitt, E Saunders

Year: 2025

Published in: Science, 2025 - science.org

Institution: London School of Economics and Political Science, Stony Brook University

Research Area: Political Persuasion with Conversational AI, LLM, Factual Accuracy in AI Systems.

Discipline: Political Science, Computational Social Science

This Science paper shows that conversational AI chatbots can systematically influence political opinions at scale, and that techniques like post-training and prompting make them far more persuasive—but that increased persuasion is tied to reduced factual accuracy in what the AI says.

Citations: 12
Going viral: Sharing of misinformation by social media influencers

Authors: R Mulcahy, R Barnes

Year: 2025

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

Institution: University of the Sunshine Coast

Research Area: Social Media, Misinformation, Influencer Marketing

Discipline: Social Science

The paper investigates how misinformation shared by social media influencers garners virality and impacts perceived deception, parasocial interactions, and sharing intent, highlighting the role of appraisals and user comments.

Methods: Three online experimental studies grounded in social influence theory and cognitive appraisal theory (CAT), analyzing user behavior in response to influencer posts with varying levels of virality and comment types.

Key Findings: Virality of posts, perceived deception, parasocial interaction, sharing intent, and effects of user comments (critical vs. supportive).

Citations: 9
The Impact of Generative AI on Social Media: An Experimental Study

Authors: AG Møller, DM Romero, D Jurgens

Year: 2025

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

Institution: University of Copenhagen, University of Michigan, Pioneer Centre for AI

Research Area: Generative AI, Social Media, Human-Computer Interaction (HCI)

Discipline: Computational Social Science

Generative AI tools on social media increase user engagement and content volume but reduce perceived quality and authenticity in discussions, highlighting challenges for ethical integration.

Methods: Controlled experiment with participants assigned to small discussion groups under distinct AI-assisted treatment conditions including chat assistance, conversation starters, feedback on comment drafts, and reply suggestions.

Key Findings: Impact of generative AI tools on user behavior, engagement, content volume, perceived quality, and authenticity in social media interactions.

DOI: https://doi.org/10.48550/arXiv.2506.14295

Citations: 9

Sample Size: 680
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
Large Language Models are often politically extreme, usually ideologically inconsistent, and persuasive even in informational contexts

Authors: N Aldahoul, H Ibrahim, M Varvello, A Kaufman

Year: 2025

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

Institution: Delft University of Technology, University of Pennsylvania, New York University, King Abdullah University of Science and Technology, Massachusetts Institute of Technology, University of Texas at Austin

Research Area: Artificial Intelligence, Computers and Society, Political Science

Discipline: Artificial Intelligence, Social Science

The study finds that Large Language Models (LLMs) exhibit extreme political views on specific topics despite appearing ideologically moderate overall, and demonstrate a persuasive influence on users' political preferences even in informational contexts.

Methods: Compared 31 LLMs' political biases against benchmarks (legislators, judges, representative voter samples) and conducted a randomized experiment to measure their persuasive impact in informational interactions.

Key Findings: Ideological consistency, political extremity, and persuasive effects of LLMs in information-seeking contexts.

Citations: 7

Sample Size: 31
Made with AI: Consumer Engagement with Social Media Containing AI Disclosures

Authors: S Carney, I Riveros, S Tully

Year: 2025

Published in: Available at SSRN 4988760, 2025 - papers.ssrn.com

Institution: University of Southern California

Research Area: Consumer Engagement with AI Disclosures, Social Media Marketing, Social Psychology

Discipline: Social Science

AI-generated content disclosures on social media reduce consumer engagement primarily due to a decrease in parasocial connections, as users perceive creators to exert less effort; signaling greater effort can mitigate this effect.

Methods: Analysis of TikTok engagement data following AIGC disclosure implementation, supplemented by six preregistered experiments.

Key Findings: Impact of AIGC disclosures on consumer engagement and the mediating role of parasocial connections.

Citations: 6
When AI is Fairer Than Humans: The Role of Egocentrism in Moral and Fairness Judgments of AI and Human Decisions

Authors: K Miazek, K Bocian

Year: 2025

Published in: Computers in Human Behavior Reports, 2025 - Elsevier

Institution: SWPS University

Research Area: Moral and Fairness Judgments of AI, Human Behavior, Egocentrism

Discipline: Social Science, Artificial Intelligence

The study found that egocentric biases influence fairness judgments, favoring decisions beneficial to self-interest, and that this bias is weaker for AI compared to human agents due to reduced perceived mind and liking for AI.

Methods: Three experiments with manipulated self-interest conditions analyzed perceptions of fairness and morality in decisions made by AI versus human agents using Prolific US samples.

Key Findings: Fairness and moral judgments in financial decision-making by AI and human agents, moderated by self-interest and social perceptions.

DOI: https://doi.org/10.1016/j.chbr.2025.100719

Citations: 6

Sample Size: 1880