Beyond Belief Change: The Persuasive Returns of Targeting Attitude–Relevant Beliefs

A persistent puzzle in the study of public opinion is why political information often produces minimal attitude change despite reliably influencing beliefs. We argue that this duality reflects belief relevance—the extent to which specific beliefs bear on attitudes. Using conversations with large language models (LLMs), we elicit deeply held issue attitudes and the “focal beliefs” people use to justify those attitudes. We then randomly assign participants to receive an LLM-generated counterargument targeting either their focal belief, an issue-relevant but unmentioned belief (“distal belief”), or a placebo. In experiments with two large online convenience samples, counterarguments targeting the aforementioned beliefs successfully decrease belief strength, with effects persisting after 10 days. More importantly, focal belief counterarguments produce larger and more durable attitude change than distal counterarguments. These findings suggest that political information can successfully shift political attitudes and provide new evidence for the role of belief relevance in persuasion.

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Publication Details

Journal
American Political Science Review
Published
2026-06-17
DOI
https://doi.org/10.1017/s0003055426101622
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00

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article

Beyond Belief Change: The Persuasive Returns of Targeting Attitude–Relevant Beliefs

Scott Clifford, Patrick Liu, Yamil Velez
American Political Science Review
Misinformation and Its Impacts
article

Beyond Belief Change: The Persuasive Returns of Targeting Attitude–Relevant Beliefs

Scott Clifford, Patrick Liu, Yamil Velez
article en

Abstract

A persistent puzzle in the study of public opinion is why political information often produces minimal attitude change despite reliably influencing beliefs. We argue that this duality reflects belief relevance—the extent to which specific beliefs bear on attitudes. Using conversations with large language models (LLMs), we elicit deeply held issue attitudes and the “focal beliefs” people use to justify those attitudes. We then randomly assign participants to receive an LLM-generated counterargument targeting either their focal belief, an issue-relevant but unmentioned belief (“distal belief”), or a placebo. In experiments with two large online convenience samples, counterarguments targeting the aforementioned beliefs successfully decrease belief strength, with effects persisting after 10 days. More importantly, focal belief counterarguments produce larger and more durable attitude change than distal counterarguments. These findings suggest that political information can successfully shift political attitudes and provide new evidence for the role of belief relevance in persuasion.

American Political Science Review
Mitchell Institute (US), Columbia University (US), Texas A&M University (US)
York University, Harvard Kennedy School
Openalex Percentile: Top 4%
Misinformation and Its Impacts
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