基于心理学理论构建可解释的说服力分析框架
Persuasion Index: A Theory-Guided Framework for Persuasion Analysis
- 从15个理论维度出发，用55个规则特征量化说服线索
- 在4个不同数据集上验证了其对说服效果的预测能力
- 开源工具支持人工与AI沟通内容的透明分析
识别说服性修辞线索在信息操控检测、AI安全和公共健康传播中至关重要。本文提出说服指数（Persuasion Index, PI），一个基于心理学与传播学说服理论的15维分类体系，包含55个由词典和规则检测器构建的子特征，具有模块化设计：可替换单个检测器而不破坏理论结构。我们在四个英文论证文本公开数据集上评估PI，这些数据集涵盖不同领域、风格和评估指标。结果表明，PI能提供共享特征空间，用于解释与说服相关结果的修辞模式。线性模型显示PI特征具备显著预测能力且计算开销低。维度级分析揭示多个数据集间普遍存在的维度-说服结果关联，也发现主题与立场相关的差异。我们已将PI作为开源包和网页界面发布，支持对人类及AI媒介沟通进行有原则、可审计的分析。
原文摘要 · Abstract (English)
Identifying persuasive rhetorical cues is critical across domains, from detecting information manipulation and improving AI safety to advancing public health communication. We propose the 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. We evaluate PI on four public datasets for English argumentative text that vary in domain, style, and outcome measures, and 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.
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