Persuasion: Research Area — Prolific Citations Library

Discover 18 peer-reviewed studies in Persuasion (2023–2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 18 peer-reviewed papers in the research area of Persuasion in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (18 of 18)

On the conversational persuasiveness of GPT-4

Authors: F Salvi, M Horta Ribeiro, R Gallotti, R West

Year: 2025

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

Institution: EPFL, Fondazione Bruno Kessle, Princeton University

Research Area: Conversational Persuasion of LLM, Human-Computer Interaction (HCI), Behavioral Science, LLM

Discipline: Behavioral Science

GPT-4 can use personalized arguments to be more persuasive in debates, outperforming humans in 64.4% of AI-human comparisons when personalization is applied.

Methods: Preregistered controlled study involving multiround debates with random assignment to conditions focusing on AI-human comparisons, personalization, and opinion strength.

Key Findings: Effectiveness of persuasion by GPT-4, especially when using personalized arguments, compared to humans in debates.

Citations: 65

Sample Size: 900
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
Comparing the persuasiveness of role-playing large language models and human experts on polarized US political issues

Authors: K Hackenburg, L Ibrahim, BM Tappin, M Tsakiris

Year: 2025

Published in: AI & SOCIETY, 2025 - Springer

Institution: Oxford Internet Institute, University of Oxford

Research Area: Political Communication and Persuasion, LLM

Discipline: Political Science, Artificial Intelligence

GPT-4's ability to generate persuasive messages rivaled human experts on polarized US political issues, suggesting AI tools may have significant implications for political campaigns and democracy.

Methods: Pre-registered experiment where GPT-4 generated partisan role-playing persuasive messages, which were compared to those from human persuasion experts.

Key Findings: Persuasive impact of GPT-4-generated messages versus human expert messages on U.S. political issues.

Citations: 35

Sample Size: 4955
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
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
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
Ethics and Persuasion in Reinforcement Learning from Human Feedback: A Procedural Rhetorical Approach

Authors: S Lodoen, A Orchard

Year: 2025

Published in: arXiv preprint arXiv:2505.09576, 2025 - arxiv.org

Institution: Embry–Riddle Aeronautical University, University of Waterloo

Research Area: Reinforcement Learning from Human Feedback (RLHF), Procedural Rhetoric, LLM Persuasion, Ethics

Discipline: Artificial Intelligence, AI Ethics, Social Science

The paper uses procedural rhetoric to analyze how RLHF reshapes ethical, social, and rhetorical dimensions of generative AI interactions, raising concerns about biases, hegemonic language, and human relationships.

Methods: The study conducts a theoretical and rhetorical analysis based on Ian Bogost's concept of procedural rhetoric, examining how RLHF mechanisms influence language conventions, information practices, and social expectations.

Key Findings: Ethical and rhetorical implications of RLHF-enhanced LLMs on language usage, information seeking, and interpersonal dynamics.

DOI: https://doi.org/10.48550/arXiv.2505.09576

Citations: 3
Persuading voters using human-artificial intelligence dialogues

Authors: H Lin, G Czarnek, B Lewis, JP White, AJ Berinsky

Year: 2025

Published in: Nature, 2025 - nature.com

Institution: Massachusetts Institute of Technology

Research Area: Political Persuasion, Human-AI Dialogue, Electoral Behavior

Discipline: Political Science, Artificial Intelligence

The study shows that AI-driven dialogues can significantly influence voter attitudes and candidate preferences, with persuasion effects surpassing traditional political advertisements, though inaccuracies were more prevalent in content generated by AI models supporting right-wing candidates.

Methods: Pre-registered experiments where participants interacted with AI advocating for one of two candidates or a ballot measure, examining persuasion strategies and effects across three elections.

Key Findings: Influence of AI-generated dialogues on voter attitudes and preferences, including analysis of persuasion strategies and accuracy of presented information.

Citations: 3
Towards Strategic Persuasion with Language Models

Authors: Z Cheng, J You

Year: 2025

Published in: arXiv preprint arXiv:2509.22989, 2025 - arxiv.org

Institution: University of Southern California, University of California Berkeley

Research Area: Artificial Intelligence, Computers and Society, Computer Science and Game Theory, Strategic Persuasion, Reinforcement Learning, Language Models, LLM, RLHF

Discipline: Artificial Intelligence

This paper introduces a scalable framework, utilizing Bayesian Persuasion, to evaluate and train LLMs for strategic persuasion, demonstrating significant persuasion gains and effective strategies through reinforcement learning.

Methods: Repurposed human-human persuasion datasets for evaluation and training; applied Bayesian Persuasion framework; used reinforcement learning to optimize LLMs for strategic persuasion.

Key Findings: The persuasive capabilities and strategies of large language models (LLMs) in various settings.

Citations: 1
A meta-analysis of the persuasive power of large language models

Authors: L Hölbling, S Maier, S Feuerriegel

Year: 2025

Published in: Scientific Reports, 2025 - nature.com

Institution: University of Lausanne, University of Zurich, University of St. Gallen

Research Area: LLMs in Persuasion, Meta-Analysis, Artificial Intelligence, Human-Computer Interaction (HCI)

Discipline: Artificial Intelligence

Large language models (LLMs) demonstrate similar persuasive performance to humans overall, but their effectiveness varies widely based on contextual factors such as model type, conversation design, and domain.

Methods: Systematic review and meta-analysis using Hedges' g to compute standardized effect sizes, with exploratory moderator analyses and publication bias checks (Egger's test, trim-and-fill analysis).

Key Findings: The persuasive effectiveness of LLMs compared to humans across various contexts and studies.

Sample Size: 17422
LLM-Generated Ads: From Personalization Parity to Persuasion Superiority

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
The potential of generative AI for personalized persuasion at scale

Authors: SC Matz, JD Teeny, SS Vaid, H Peters, GM Harari

Year: 2024

Published in: Scientific Reports, 2024 - nature.com

Institution: Stanford University

Research Area: Personalized Persuasion, Generative AI, Political Influence

Discipline: Artificial Intelligence

Generative AI, specifically large language models like ChatGPT, effectively scale personalized persuasion by matching messages to psychological profiles, demonstrating increased influence across domains and profiles.

Methods: Four studies (with seven sub-studies) tested personalized persuasive messaging crafted by ChatGPT against non-personalized messages across various psychological and domain-specific dimensions.

Key Findings: Effectiveness of personalized persuasive messages crafted by generative AI in different domains, targeting psychological profiles such as personality traits, political ideology, and moral foundations.

Citations: 368

Sample Size: 1788
On the conversational persuasiveness of large language models: A randomized controlled trial

Authors: F Salvi, MH Ribeiro, R Gallotti

Year: 2024

Published in: arXiv preprint arXiv ..., 2024 - atelierdesfuturs.org

Institution: EPFL, Fondazione Bruno Kessle

Research Area: Conversational Persuasion in LLM

Discipline: Artificial Intelligence

The study demonstrates that GPT-4 is highly persuasive in direct conversations, especially when equipped with personalized sociodemographic information about its opponent, raising concerns about its potential misuse in personalized persuasion contexts.

Methods: Participants engaged in multiple-round debates on a web-based platform under randomized conditions, with comparisons between human-human and human-AI interactions and the impact of personalization.

Key Findings: The persuasiveness of GPT-4 compared to humans, with and without personalization using sociodemographic data.

Citations: 118

Sample Size: 820
Evidence of a log scaling law for political persuasion with large language models

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

Year: 2024

Published in: arXiv preprint arXiv ..., 2024 - arxiv.org

Institution: University of Oxford, The Alan Turing Institute, Royal Holloway, University of London, Bocconi University, Meedan

Research Area: LLM scaling laws, Political Persuasion, LLM, AI Social Science

Discipline: Political Science, Artificial Intelligence

Persuasiveness of messages generated by large language models follows a log scaling law with diminishing returns as model size increases, and task completion appears to primarily drive this capability.

Methods: Generated 720 persuasive messages on 10 U.S. political issues using 24 language models of varying sizes; evaluated persuasiveness through a large-scale randomized survey experiment.

Key Findings: Persuasiveness of large language model-generated political messages across different model sizes.

Citations: 17

Sample Size: 25982
Large language models can enhance persuasion through linguistic feature alignment

Authors: M Shin, J Kim

Year: 2024

Published in: Available at SSRN 4725351, 2024 - researchgate.net

Institution: Massachusetts Institute of Technology, Yale University

Research Area: Linguistic Feature Alignment, Persuasion, LLM

Discipline: Artificial Intelligence, Computational Social Science

Citations: 11
Evaluating the persuasive influence of political microtargeting with large language models

Authors: K Hackenburg, H Margetts

Year: 2023

Published in: Proceedings of the National Academy of ..., 2024 - pnas.org

Institution: Oxford University, Alan Turing Institute

Research Area: Political Persuasion, LLM, Political Science

Discipline: Political Science

DOI: https://doi.org/10.1073/pnas.2403116121

Citations: 153
Artificial intelligence can persuade humans on political issues

Authors: H Bai, J Voelkel, J Eichstaedt, R Willer

Year: 2023

Published in: 2023 - researchsquare.com

Institution: Stanford University, London Business School, Dartmouth College, Stanford Graduate School of Business

Research Area: Political Persuasion, Social Influence of AI, Cognitive Science

Discipline: Political Science, Social Science

Citations: 100
Large language models respond to influence like humans

Authors: L Griffin, B Kleinberg, M Mozes, K Mai

Year: 2023

Published in: Proceedings of the ..., 2023 - aclanthology.org

Institution: University College London, Tilburg University

Research Area: LLM Influence and Persuasion, LLM

Discipline: Social Science

Citations: 25