# PCoT: Persuasion-Augmented Chain of Thought for Detecting Fake News and Social Media Disinformation

arXiv: https://arxiv.org/abs/2506.06842 · HTML: https://mlsanity.com/paper/2506.06842/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 75 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [With Little Power Comes Great Responsibility](https://mlsanity.com/paper/2010.06595/) (2020)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves](https://mlsanity.com/paper/2311.04205/) (2023)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation](https://mlsanity.com/paper/2310.15515/) (2023)
- [Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4](https://mlsanity.com/paper/2305.14928/) (2023)
- [Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?](https://mlsanity.com/paper/2208.09214/) (2022)
- [Cross-SEAN: A Cross-Stitch Semi-Supervised Neural Attention Model for COVID-19 Fake News Detection](https://mlsanity.com/paper/2102.08924/) (2021)
- [EUvsDisinfo: A Dataset for Multilingual Detection of Pro-Kremlin Disinformation in News Articles](https://mlsanity.com/paper/2406.12614/) (2024)
- [CLUES: A Benchmark for Learning Classifiers using Natural Language Explanations](https://mlsanity.com/paper/2204.07142/) (2022)
- [Using Persuasive Writing Strategies to Explain and Detect Health Misinformation](https://mlsanity.com/paper/2211.05985/) (2022)
- [Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets](https://mlsanity.com/paper/2104.05321/) (2021)
- [A Cross-Domain Study of the Use of Persuasion Techniques in Online Disinformation](https://mlsanity.com/paper/2412.15098/) (2024)
- [Company classification using zero-shot learning](https://mlsanity.com/paper/2305.01028/) (2023)

## Built on by (most cited first, 11 of 11)

- [Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference](https://mlsanity.com/paper/2512.01728/) (2025)
- [The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence](https://mlsanity.com/paper/2601.05478/) (2026)
- [Detecting Winning Arguments with Large Language Models and Persuasion Strategies](https://mlsanity.com/paper/2601.10660/) (2026)
- [Agentic Multi-Persona Framework for Evidence-Aware Fake News Detection](https://mlsanity.com/paper/2512.21039/) (2025)
- [Can AI-Generated Persuasion Be Detected? Persuaficial Benchmark and AI vs. Human Linguistic Differences](https://mlsanity.com/paper/2601.04925/) (2026)
- [Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection](https://mlsanity.com/paper/2605.19285/) (2026)
- [Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection](https://mlsanity.com/paper/2605.12156/) (2026)
- [ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies](https://mlsanity.com/paper/2605.27045/) (2026)
- [A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing](https://mlsanity.com/paper/2606.05330/) (2026)
- [Persuasion Index: A Theory-Guided Framework for Persuasion Analysis](https://mlsanity.com/paper/2606.14580/) (2026)
- [A Corpus of Persuasion Techniques in Slavic Languages](https://mlsanity.com/paper/2607.10715/) (2026)

## Abstract (arXiv)

Disinformation detection is a key aspect of media literacy. Psychological studies have shown that knowledge of persuasive fallacies helps individuals detect disinformation. Inspired by these findings, we experimented with large language models (LLMs) to test whether infusing persuasion knowledge enhances disinformation detection. As a result, we introduce the Persuasion-Augmented Chain of Thought (PCoT), a novel approach that leverages persuasion to improve disinformation detection in zero-shot classification. We extensively evaluate PCoT on online news and social media posts. Moreover, we publish two novel, up-to-date disinformation datasets: EUDisinfo and MultiDis. These datasets enable the evaluation of PCoT on content entirely unseen by the LLMs used in our experiments, as the content was published after the models' knowledge cutoffs. We show that, on average, PCoT outperforms competitive methods by 15% across five LLMs and five datasets. These findings highlight the value of persuasion in strengthening zero-shot disinformation detection.