# Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection

arXiv: https://arxiv.org/abs/2510.09695 · HTML: https://mlsanity.com/paper/2510.09695/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 68 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 23 of 23)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [Aspect-Controlled Neural Argument Generation](https://mlsanity.com/paper/2005.00084/) (2020)
- [GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception](https://mlsanity.com/paper/1912.03184/) (2019)
- [Before Name-calling: Dynamics and Triggers of Ad Hominem Fallacies in Web Argumentation](https://mlsanity.com/paper/1802.06613/) (2018)
- [Measuring and Benchmarking Large Language Models' Capabilities to Generate Persuasive Language](https://mlsanity.com/paper/2406.17753/) (2024)
- [Argotario: Computational Argumentation Meets Serious Games](https://mlsanity.com/paper/1707.06002/) (2017)
- [Are Large Language Models Reliable Argument Quality Annotators?](https://mlsanity.com/paper/2404.09696/) (2024)
- [How susceptible are LLMs to Logical Fallacies?](https://mlsanity.com/paper/2308.09853/) (2023)
- [PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetry](https://mlsanity.com/paper/2003.07723/) (2020)
- [Missci: Reconstructing Fallacies in Misrepresented Science](https://mlsanity.com/paper/2406.03181/) (2024)
- [Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding](https://mlsanity.com/paper/2404.04293/) (2024)
- [Fearful Falcons and Angry Llamas: Emotion Category Annotations of Arguments by Humans and LLMs](https://mlsanity.com/paper/2412.15993/) (2024)
- [Large Language Models Are Better Logical Fallacy Reasoners with Counterargument, Explanation, and Goal-Aware Prompt Formulation](https://mlsanity.com/paper/2503.23363/) (2025)
- [LiTransProQA: an LLM-based Literary Translation evaluation metric with Professional Question Answering](https://mlsanity.com/paper/2505.05423/) (2025)
- [Boosting Logical Fallacy Reasoning in LLMs via Logical Structure Tree](https://mlsanity.com/paper/2410.12048/) (2024)
- [A Logical Fallacy-Informed Framework for Argument Generation](https://mlsanity.com/paper/2408.03618/) (2024)
- [Grounding Fallacies Misrepresenting Scientific Publications in Evidence](https://mlsanity.com/paper/2408.12812/) (2024)
- [Fine-grained Fallacy Detection with Human Label Variation](https://mlsanity.com/paper/2502.13853/) (2025)
- [Do Emotions Really Affect Argument Convincingness? A Dynamic Approach with LLM-based Manipulation Checks](https://mlsanity.com/paper/2503.00024/) (2025)
- [Trust Me, I Can Convince You: The Contextualized Argument Appraisal Framework](https://mlsanity.com/paper/2509.17844/) (2025)

## Built on by (most cited first, 3 of 3)

- [Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs](https://mlsanity.com/paper/2605.29928/) (2026)
- [Categorical Emotions or Appraisals - Which Emotion Model Explains Argument Convincingness Better?](https://mlsanity.com/paper/2511.07162/) (2025)
- [Big-Big-SycoBench: A Benchmark to Detect Sycophancy Through Model Responses to Valid vs. Invalid Arguments](https://mlsanity.com/paper/2610.15634/) (2026)

## Abstract (arXiv)

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.