Discover 17 peer-reviewed studies in Computational Social Science (2023–2026). Explore research findings powered by Prolific's diverse participant panel.
This page lists 17 peer-reviewed papers in the research area of Computational Social Science in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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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
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
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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
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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, 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
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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
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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
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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
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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
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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.
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Authors: D Guilbeault, S Delecourt, T Hull, BS Desikan, M Chu
Year: 2024
Published in: Nature, 2024 - nature.com
Institution: University of California Berkeley, Institute For Public Policy Research, Columbia University, University of Southern California Los Angeles
Research Area: Gender Bias, Computational Social Science, Online Media, AI Bias
Discipline: Computational Social Science
Online images significantly amplify gender bias compared to text, with biases in visual content impacting societal beliefs about gender roles.
Methods: Analyzed 3,495 social categories using over one million images from platforms like Google, Wikipedia, and IMDb, compared visual content to billions of words from the same platforms, and conducted a preregistered national experiment to assess the psychological impact on participants' beliefs.
Key Findings: The prevalence and psychological impact of gender bias in online images compared to text, including gender associations and representation disparities.
DOI: https://doi.org/10.1038/s41586-024-07068-x
Citations: 72
Sample Size: 3495
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Authors: F Zanartu, J Cook, M Wagner, J Garcia
Year: 2024
Published in: ArXiv
Institution: Monash University, University of Melbourne
Research Area: Artificial Intelligence, Computational Social Science, Misinformation Detection, Fallacy Analysis in Climate Communication.
Discipline: Artificial Intelligence, Computational Social Science
Citations: 6
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Authors: T Davidson
Year: 2024
Published in: 2024 - files.osf.io
Institution: University of Cambridge
Research Area: Content Moderation, Multimodal LLM Auditing, Computational Social Science
Discipline: Computational Social Science
Citations: 2
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Authors: J Agley
Year: 2024
Published in: Evaluation & the Health Professions, 2025 - journals.sagepub.com
Institution: Indiana University, Prevention Insights
Research Area: Health Research and Evaluation, Data Validity, Computational Social Science
Discipline: Public Health, Computational Social Science
Citations: 2
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Authors: Eddie L. Ungless, Nina Markl, Björn Ross
Year: 2024
Published in: ArXiv
Institution: University of Edinburgh, University of Essex
Research Area: Computational Social Science, Human-Computer Interaction, Media Studies
Discipline: Computational Social Science
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Authors: HR Kirk, C Osborne
Year: 2024
Published in: ArXiv
Institution: Alan Turing Institute, Oxford Internet Institute, Oxford University
Research Area: Computational Social Science, AI Community Analysis, Hugging Face Hub Activity
Discipline: Computational Social Science
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Authors: C Wang, SK Freire, M Zhang, J Wei
Year: 2023
Published in: arXiv preprint arXiv ..., 2023 - arxiv.org
Institution: Delft University of Technolog, University of Melbourne
Research Area: Human-Computer Interaction, Computational Social Science, AI Security
Discipline: Human-Computer Interaction
DOI: https://doi.org/10.48550/arXiv.2306.08833
Citations: 18
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Authors: T Kaufmann, S Ball, J Beck, E Hüllermeier
Year: 2023
Published in: ... European Conference on ..., 2023 - Springer
Research Area: Reinforcement Learning, Artificial Intelligence, Computational Social Science
Discipline: Artificial Intelligence, Computational Social Science
DOI: https://doi.org/10.1007/978-3-031-74627-7_21
Citations: 14
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Authors: Chiara Drolsbach, Nicolas Pröllochs
Year: 2023
Published in: ArXiv
Institution: JLU Giessen
Research Area: Computational Social Science, Misinformation, Social Media Analysis
Discipline: Computational Social Science
