Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908906251354112 |
|---|---|
| author | Chen, Yanran Greschner, Lynn Klinger, Roman Klenk, Michael Eger, Steffen |
| author_facet | Chen, Yanran Greschner, Lynn Klinger, Roman Klenk, Michael Eger, Steffen |
| contents | Logical fallacies are common in public communication and can mislead audiences; fallacious arguments may still appear convincing despite lacking soundness, because convincingness is inherently subjective. We present the first computational study of how emotional framing interacts with fallacies and convincingness, using large language models (LLMs) to systematically change emotional appeals in fallacious arguments. We benchmark eight LLMs on injecting emotional appeal into fallacious arguments while preserving their logical structures, then use the best models to generate stimuli for a human study. Our results show that LLM-driven emotional framing reduces human fallacy detection in F1 by 14.5% on average. Humans perform better in fallacy detection when perceiving enjoyment than fear or sadness, and these three emotions also correlate with significantly higher convincingness compared to neutral or other emotion states. Our work has implications for AI-driven emotional manipulation in the context of fallacious argumentation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09695 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection Chen, Yanran Greschner, Lynn Klinger, Roman Klenk, Michael Eger, Steffen Computation and Language Logical fallacies are common in public communication and can mislead audiences; fallacious arguments may still appear convincing despite lacking soundness, because convincingness is inherently subjective. We present the first computational study of how emotional framing interacts with fallacies and convincingness, using large language models (LLMs) to systematically change emotional appeals in fallacious arguments. We benchmark eight LLMs on injecting emotional appeal into fallacious arguments while preserving their logical structures, then use the best models to generate stimuli for a human study. Our results show that LLM-driven emotional framing reduces human fallacy detection in F1 by 14.5% on average. Humans perform better in fallacy detection when perceiving enjoyment than fear or sadness, and these three emotions also correlate with significantly higher convincingness compared to neutral or other emotion states. Our work has implications for AI-driven emotional manipulation in the context of fallacious argumentation. |
| title | Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2510.09695 |