# CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit

arXiv: https://arxiv.org/abs/2410.12061 · HTML: https://mlsanity.com/paper/2410.12061/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 50 references that are in the map.
- First posted on arXiv 15 Oct 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate](https://mlsanity.com/paper/2402.07401/) (2024)
- [Graph Neural Networks with Continual Learning for Fake News Detection from Social Media](https://mlsanity.com/paper/2007.03316/) (2020)
- [Language Models Hallucinate, but May Excel at Fact Verification](https://mlsanity.com/paper/2310.14564/) (2023)
- [Cheap-fake Detection with LLM using Prompt Engineering](https://mlsanity.com/paper/2306.02776/) (2023)
- [RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information](https://mlsanity.com/paper/2406.11093/) (2024)
- [Web Retrieval Agents for Evidence-Based Misinformation Detection](https://mlsanity.com/paper/2409.00009/) (2024)
- [Credibility-based Fake News Detection](https://mlsanity.com/paper/1911.00643/) (2019)
- [Evaluation of Fake News Detection with Knowledge-Enhanced Language Models](https://mlsanity.com/paper/2204.00458/) (2022)
- [Heterogeneous Subgraph Transformer for Fake News Detection](https://mlsanity.com/paper/2404.13192/) (2024)
- [Curating Grounded Synthetic Data with Global Perspectives for Equitable AI](https://mlsanity.com/paper/2406.10258/) (2024)
- [News Source Credibility Assessment: A Reddit Case Study](https://mlsanity.com/paper/2402.10938/) (2024)
- [Susceptibility of Communities against Low-Credibility Content in Social News Websites](https://mlsanity.com/paper/2403.10705/) (2024)

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

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Veracity: An Open-Source AI Fact-Checking System](https://mlsanity.com/paper/2506.15794/) (2025)
- [Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions](https://mlsanity.com/paper/2604.12138/) (2026)

## Abstract (arXiv)

Fake news threatens democracy and exacerbates the polarization and divisions in society; therefore, accurately detecting online misinformation is the foundation of addressing this issue. We present CrediRAG, the first fake news detection model that combines language models with access to a rich external political knowledge base with a dense social network to detect fake news across social media at scale. CrediRAG uses a news retriever to initially assign a misinformation score to each post based on the source credibility of similar news articles to the post title content. CrediRAG then improves the initial retrieval estimations through a novel weighted post-to-post network connected based on shared commenters and weighted by the average stance of all shared commenters across every pair of posts. We achieve 11% increase in the F1-score in detecting misinformative posts over state-of-the-art methods. Extensive experiments conducted on curated real-world Reddit data of over 200,000 posts demonstrate the superior performance of CrediRAG on existing baselines. Thus, our approach offers a more accurate and scalable solution to combat the spread of fake news across social media platforms.