Testing theories of political persuasion using AI.
Journal:
Proceedings of the National Academy of Sciences of the United States of America
PMID:
40314974
Abstract
Despite its importance to society and many decades of research, key questions about the social and psychological processes of political persuasion remain unanswered, often due to data limitations. We propose that AI tools, specifically generative large language models (LLMs), can be used to address these limitations, offering important advantages in the study of political persuasion. In two preregistered online survey experiments, we demonstrate the potential of generative AI as a tool to study persuasion and provide important insights about the psychological and communicative processes that lead to increased persuasion. Specifically, we test the effects of four AI-generated counterattitudinal persuasive strategies, designed to test the effectiveness of messages that include customization (writing messages based on a receiver's personal traits and beliefs), and elaboration (increased psychological engagement with the argument through interaction). We find that all four types of persuasive AI produce significant attitude change relative to the control and shift vote support for candidates espousing views consistent with the treatments. However, we do not find evidence that message customization via microtargeting or cognitive elaboration through interaction with the AI have much more persuasive effect than a single generic message. These findings have implications for different theories of persuasion, which we discuss. Finally, we find that although persuasive messages are able to moderate some people's attitudes, they have inconsistent and weaker effects on the democratic reciprocity people grant to their political opponents. This suggests that attitude moderation (ideological depolarization) does not necessarily lead to increased democratic tolerance or decreased affective polarization.