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Testing theories of political persuasion using AI
Lisa P Argyle1, Ethan C Busby1, Joshua R Gubler1
1Department of Political Science, Brigham Young University, Provo, UT 84602.
Summary
Generative artificial intelligence (AI) can aid political persuasion research. AI-generated messages shift attitudes, but customization and interaction offer minimal advantages over generic content.
Area of Science:
- Political Science
- Psychology
- Artificial Intelligence
Background:
- Political persuasion research faces data limitations hindering understanding of social and psychological processes.
- Generative large language models (LLGs) offer novel solutions to overcome data constraints in persuasion studies.
Purpose of the Study:
- To investigate the efficacy of generative AI in political persuasion research.
- To evaluate AI-generated persuasive strategies, including message customization and elaboration, on attitude change and vote support.
Main Methods:
- Two preregistered online survey experiments were conducted.
- Four types of AI-generated counterattitudinal persuasive messages were tested.
- Message customization (based on personal traits) and elaboration (through interaction) were examined.
Main Results:
- All AI-generated persuasive strategies significantly changed attitudes and vote support compared to a control group.
- Message customization and AI interaction did not yield substantially greater persuasion than a generic message.
- Persuasive messages moderated attitudes but had limited impact on democratic reciprocity and affective polarization.
Conclusions:
- Generative AI is a viable tool for studying political persuasion, offering insights into psychological and communicative processes.
- The effectiveness of AI-driven persuasion is not significantly enhanced by message customization or interactive elaboration.
- Attitude moderation does not automatically translate to increased democratic tolerance or reduced affective polarization.
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