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.

翻译：识别有说服力的修辞线索在多个领域至关重要，从检测信息操纵、提升人工智能安全性，到促进公共卫生传播。我们提出说服力指数（PI），这是一个基于心理学与传播学说服理论的15个维度分类体系，并通过55个基于词典和规则检测器的子特征实现透明化。该分类体系采用模块化设计：在保留理论结构的同时，各独立检测器可被替换。通过在四个覆盖不同领域、风格及结果指标的公开数据集上评估PI，我们证明PI为解释与说服相关结果关联的修辞模式提供了共享特征空间。线性模型表明，PI特征在保持计算轻量化的同时蕴含显著预测信号。维度层面分析揭示了跨数据集中PI维度与说服结果之间的重复关联模式，同时也凸显了主题和立场特异性差异。我们以开源工具包和网页界面的形式发布PI，用于对人类与AI中介传播进行原则性且可审计的分析。