Hosted by the DSI Computational Social Science Working Group as part of their Research & Networking Series
Speaker: Yamil Velez, Assistant Professor of Political Science
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Beyond Belief Change: The Persuasive Returns of Targeting Attitude-Relevant Beliefs
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. Drawing on semi-structured conversations with large language models, 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 ten 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.