# An Explainable Framework for Misinformation Identification via Critical Question Answering

arXiv: https://arxiv.org/abs/2503.14626 · HTML: https://mlsanity.com/paper/2503.14626/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [Disinformation Detection: An Evolving Challenge in the Age of LLMs](https://mlsanity.com/paper/2309.15847/) (2023)
- [Multitask Instruction-based Prompting for Fallacy Recognition](https://mlsanity.com/paper/2301.09992/) (2023)
- [Neural Generative Question Answering](https://mlsanity.com/paper/1512.01337/) (2015)
- [Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation](https://mlsanity.com/paper/2011.13187/) (2020)
- [Hoaxy: A Platform for Tracking Online Misinformation](https://mlsanity.com/paper/1603.01511/) (2016)
- [Two is Better than Many? Binary Classification as an Effective Approach to Multi-Choice Question Answering](https://mlsanity.com/paper/2210.16495/) (2022)
- [Anatomy of an online misinformation network](https://mlsanity.com/paper/1801.06122/) (2018)

## Built on by (most cited first, 4 of 4)

- [Beyond Logical Forms: LLM-Extracted Patterns for Fallacy Classification](https://mlsanity.com/paper/2606.26698/) (2026)
- [ELLIS Alicante at CQs-Gen 2025: Winning the critical thinking questions shared task: LLM-based question generation and selection](https://mlsanity.com/paper/2506.14371/) (2025)
- [Fallacy Benchmarks Measure Scheme Recognition, Not Fallacy Detection](https://mlsanity.com/paper/2609.18644/) (2026)
- [SoK: Formal Methods for Fact-Checking and Information Integrity](https://mlsanity.com/paper/2609.23239/) (2026)

## Abstract (arXiv)

Natural language misinformation detection approaches have been, to date, largely dependent on sequence classification methods, producing opaque systems in which the reasons behind classification as misinformation are unclear. While an effort has been made in the area of automated fact-checking to propose explainable approaches to the problem, this is not the case for automated reason-checking systems. In this paper, we propose a new explainable framework for both factual and rational misinformation detection based on the theory of Argumentation Schemes and Critical Questions. For that purpose, we create and release NLAS-CQ, the first corpus combining 3,566 textbook-like natural language argumentation scheme instances and 4,687 corresponding answers to critical questions related to these arguments. On the basis of this corpus, we implement and validate our new framework which combines classification with question answering to analyse arguments in search of misinformation, and provides the explanations in form of critical questions to the human user.