Persuasion Index: A Theory-Guided Framework for Persuasion Analysis
For researchers studying persuasion in human and AI-mediated communication, PI provides a principled, auditable, and modular framework for analyzing rhetorical cues across domains.
The paper introduces Persuasion Index (PI), a theory-grounded taxonomy of 15 persuasion dimensions with a transparent implementation using 55 sub-features, and demonstrates its utility across four datasets by showing that PI features carry meaningful predictive signal in linear models while enabling interpretable analysis of rhetorical patterns.
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.