Patterns of Persuasion Through the Lens of Theory of Mind: Value Alignment Analysis in Online Deliberation

Baktash Ansari, Mouly Dewan, Vibhor Agarwal, Afra Mashhadi

Keywords: Theory of Mind, LLM Social Reasoning, Computational Persuasion, Computational Social Science, Online Deliberation, Value Alignment

Abstract: Understanding what makes an argument persuasive is central to computational social science. Yet, most approaches rely on surface-level linguistic features or uninterpretable neural classifiers. We propose a framework that examines persuasion through the lens of Theory of Mind (TOM): the cognitive capacity to model others beliefs, desires, intentions, emotions, knowledge, and perspectives. Using an LLM to extract structured TOM value profiles from 19,340 post-comment pairs on r/ChangeMyView sub-reddit, we compute fine-grained alignment features between posts and their responses. A human annotation study validates the quality of these extracted values. Our analysis reveals four distinct mechanisms of persuasion: First, persuasive comments align closely with a post's cognitive dimensions (beliefs, desires), whereas non-persuasive, highly-engaged comments rely on affective dimensions (emotions). Second, successful persuasion utilizes a "cover and reframe" strategy, addressing the author's emotional and factual concerns while introducing novel intentional framing. Third, exact lexical echoing of a user's knowledge is the strongest negative indicator of persuasion. Finally, persuasive comments exhibit higher internal TOM consistency, presenting a unified mental model where cognitive and affective dimensions reinforce one another. These findings provide theory-grounded insights into human deliberation and lay the groundwork for new, TOM-informed evaluation suites for LLM social reasoning.

Submission Type: Long Paper (archival)

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Submission Number: 8