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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Persuasion Index: A Theory-Guided Framework for Persuasion Analysis
%A Gong, Liancheng
%A Wang, Zhiyang
%A Xu, Yiwei
%A Mendelsohn, Julia
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F gong-etal-2026-persuasion
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2426/
%P 55071-55110

Markdown (Informal)

[Persuasion Index: A Theory-Guided Framework for Persuasion Analysis](https://aclanthology.org/2026.emnlp-main.2426/) (Gong et al., EMNLP 2026)

• Persuasion Index: A Theory-Guided Framework for Persuasion Analysis (Gong et al., EMNLP 2026)

ACL

• Liancheng Gong, Zhiyang Wang, Yiwei Xu, and Julia Mendelsohn. 2026. Persuasion Index: A Theory-Guided Framework for Persuasion Analysis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 55071–55110, Budapest, Hungary. Association for Computational Linguistics.