# Credibility-based Fake News Detection

arXiv: https://arxiv.org/abs/1911.00643 · HTML: https://mlsanity.com/paper/1911.00643/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2019.

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

- [Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals](https://mlsanity.com/paper/2309.07601/) (2023)
- [Identifying Informational Sources in News Articles](https://mlsanity.com/paper/2305.14904/) (2023)
- [Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Data](https://mlsanity.com/paper/2412.14276/) (2024)
- [Explaining Veracity Predictions with Evidence Summarization: A Multi-Task Model Approach](https://mlsanity.com/paper/2402.06443/) (2024)
- [Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish](https://mlsanity.com/paper/2403.00411/) (2024)
- [CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit](https://mlsanity.com/paper/2410.12061/) (2024)
- [Reasoning About the Unsaid: Misinformation Detection with Omission-Aware Graph Inference](https://mlsanity.com/paper/2512.01728/) (2025)
- [Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection](https://mlsanity.com/paper/2509.01660/) (2025)
- [FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features](https://mlsanity.com/paper/2109.04835/) (2021)
- [Fake News Detection Tools and Methods -- A Review](https://mlsanity.com/paper/2112.11185/) (2021)
- [RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation](https://mlsanity.com/paper/2401.10940/) (2024)

## Abstract (arXiv)

Fake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a network of users. In this paper, we emphasize the detection of fake news by assessing its credibility. By analyzing public fake news data, we show that information on news sources (and authors) can be a strong indicator of credibility. Our findings suggest that an author's history of association with fake news, and the number of authors of a news article, can play a significant role in detecting fake news. Our approach can help improve traditional fake news detection methods, wherein content features are often used to detect fake news.