Persuasion Index: A Theory-Guided Framework for Persuasion Analysis
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Computer Science > Computation and Language
Title:Persuasion Index: A Theory-Guided Framework for Persuasion Analysis
Abstract:Identifying persuasive rhetorical cues is critical across domains, from detecting information manipulation and improving AI safety to advancing public health communication. We propose Persuasion Index (PI), a taxonomy of 15 dimensions grounded in persuasion theories from psychology and communication, and one transparent implementation using 55 sub-features built from lexicons and rule-based detectors. The taxonomy is modular: individual detectors can be replaced while preserving the theoretical structure. By evaluating PI on four public datasets varying in domain, style, and outcome measures, we show that PI provides a shared feature space for interpreting rhetorical patterns associated with persuasion-related outcomes. Linear models show that PI features carry meaningful predictive signal while remaining computationally lightweight. Dimension-level analyses reveal recurring associations between PI dimensions and persuasion outcomes across datasets, while also highlighting topic- and stance-specific variation. We release PI as an open-source package and web interface for principled and auditable analysis of human and AI-mediated communication.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.14580 [cs.CL] |
| (or arXiv:2606.14580v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14580
arXiv-issued DOI via DataCite (pending registration)
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