arXiv:2606.14580v1 Announce Type: new 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
The increasing prevalence of AI in public discourse and its potential for manipulation necessitates robust methods for identifying persuasive cues. This research arrives as AI safety and responsible AI development gain critical attention.
This framework offers a standardized, theory-guided approach to analyze persuasion, which is vital for mitigating information manipulation, enhancing AI safety, and improving communication strategies across various sectors. It aids in understanding and potentially counteracting persuasive AI systems.
The ability to systematically analyze and quantify persuasion in language is enhanced through a modular, theoretically-grounded taxonomy. This provides a new tool for researchers and developers to understand and measure the persuasive elements of AI-generated content or human communication.
- · AI safety researchers
- · Public health communicators
- · Fact-checking organizations
- · Social media platforms
- · Malicious influence operations
- · Unregulated AI content creators
- · Entities relying on stealth persuasion
Improved detection capabilities for persuasive rhetoric in text, including AI-generated content.
Development of more resilient and less manipulable AI systems, as well as AI trained to detect persuasion effectively.
Enhanced public literacy regarding persuasive techniques, diminishing the efficacy of subtle influence campaigns and fostering more critical information consumption.
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