Papers with fallacy detection
Fine-grained Fallacy Detection with Human Label Variation (2025.naacl-long)
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| Challenge: | Fallacy detection is an open challenge in NLP and has shown to be intrinsically difficult for both humans and machines. |
| Approach: | They propose a framework that minimizes annotation errors whilst keeping signals of human label variation. |
| Outcome: | The proposed framework minimizes annotation errors while keeping signals of human label variation. |
Emotionally Charged, Logically Blurred: AI-driven Emotional Framing Impairs Human Fallacy Detection (2026.eacl-long)
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| Challenge: | Existing computational approaches focus on logical structures of fallacies and argumentation schemes, ignoring the emotional dimension of argumentation. |
| Approach: | They propose to use large language models to systematically change emotional appeals in fallacious arguments by using a computational approach. |
| Outcome: | The proposed method reduces fallacy detection by 14.5% on average on human arguments with enjoyment over fear or sadness. |
The Search for Agreement on Logical Fallacy Annotation of an Infodemic (2022.lrec-1)
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Claire Bonial, Austin Blodgett, Taylor Hudson, Stephanie M. Lukin, Jeffrey Micher, Douglas Summers-Stay, Peter Sutor, Clare Voss
| Challenge: | a parallel "infodemic" has emerged with the COVID-19 pandemic . logical fallacies can be subtly encoded in the structure of a document across multiple sentences . |
| Approach: | They evaluate an annotation schema for labeling logical fallacy types using linguist annotations . they propose to use a machine learning algorithm to train annotators for fallacy detection . |
| Outcome: | The proposed annotation schema is clear and non-overlapping for manual and system assignment. |
Boosting Logical Fallacy Reasoning in LLMs via Logical Structure Tree (2024.emnlp-main)
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| Challenge: | Logical fallacy is the use of invalid or flawed reasoning in the construction of a statement. |
| Approach: | They propose to build a logical structure tree to represent hierarchical logic flow among relation connectives and their arguments in a statement. |
| Outcome: | The proposed model significantly improves accuracy and recall for fallacy detection and fallacy classification. |