# Detection and Discovery of Misinformation Sources using Attributed Webgraphs

arXiv: https://arxiv.org/abs/2401.02379 · HTML: https://mlsanity.com/paper/2401.02379/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2024.

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

- [Bridging Social Media and Search Engines: Dredge Words and the Detection of Unreliable Domains](https://mlsanity.com/paper/2406.11423/) (2024)
- [DocNet: Semantic Structure in Inductive Bias Detection Models](https://mlsanity.com/paper/2406.10965/) (2024)
- [CrediBench: Building Web-Scale Network Datasets for Information Integrity](https://mlsanity.com/paper/2509.23340/) (2025)
- [Network Information Enhances Unreliable News Domain Detection](https://mlsanity.com/paper/2608.02399/) (2026)

## Abstract (arXiv)

Website reliability labels underpin almost all research in misinformation detection. However, misinformation sources often exhibit transient behavior, which makes many such labeled lists obsolete over time. We demonstrate that Search Engine Optimization (SEO) attributes provide strong signals for predicting news site reliability. We introduce a novel attributed webgraph dataset with labeled news domains and their connections to outlinking and backlinking domains. We demonstrate the success of graph neural networks in detecting news site reliability using these attributed webgraphs, and show that our baseline news site reliability classifier outperforms current SoTA methods on the PoliticalNews dataset, achieving an F1 score of 0.96. Finally, we introduce and evaluate a novel graph-based algorithm for discovering previously unknown misinformation news sources.