Browse 5 peer-reviewed papers from University Of California Irvine spanning Computational Social Science, Computational Linguistics (2023–2025). Research powered by Prolific's high-quality participant data.
This page lists 5 peer-reviewed papers from researchers at University Of California Irvine in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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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: E Meguellati, S Civelli, L Han, A Bernstein
Year: 2025
Published in: arXiv preprint arXiv ..., 2025 - arxiv.org
Institution: Oregon Health Sciences University, Oregon University of California, Irvine, Han Institute, NYU School of Law, Bernstein Research
Research Area: Advertising, Persuasion Strategies, Human-AI Interaction in Content Generation
Discipline: Artificial Intelligence
LLM-generated advertisements achieved parity with human-written ads in personalization and demonstrated superiority in persuasion using psychological principles, outperforming human ads even when AI-origin detection impacted results.
Methods: Two-part study: First examined LLM personalization based on personality traits; second tested psychological persuasion principles using universal messages across authority, consensus, cognition, and scarcity.
Key Findings: Effectiveness of LLM-generated ads in personalization and persuasive storytelling compared to human-created ads.
Sample Size: 1200
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Authors: HC Gordon, T Stafford, K Dommett
Year: 2024
Published in: ... of the Annual Meeting of the ..., 2024 - escholarship.org
Institution: University of California, Irvine, University of New York, Buffalo, University of Bath
Research Area: Political Advertising, Trust, Political Communication, Transparency
Discipline: Political Science, Communication
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Authors: P Pataranutaporn, R Liu, E Finn, P Maes
Year: 2023
Published in: Nature Machine Intelligence, 2023 - nature.com
Institution: University of California Irvine
Research Area: Human-AI Interaction, Behavioral Science
Discipline: Human-Computer Interaction, Artificial Intelligence
DOI: https://doi.org/10.1038/s42256-023-00720-7
Citations: 180
