# FNDaaS: Content-agnostic Detection of Fake News sites

arXiv: https://arxiv.org/abs/2212.06492 · HTML: https://mlsanity.com/paper/2212.06492/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 13 Dec 2022.

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

- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [CSI: A Hybrid Deep Model for Fake News Detection](https://mlsanity.com/paper/1703.06959/) (2017)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [Network-based Fake News Detection: A Pattern-driven Approach](https://mlsanity.com/paper/1906.04210/) (2019)
- [The Surprising Performance of Simple Baselines for Misinformation Detection](https://mlsanity.com/paper/2104.06952/) (2021)
- [Who Funds Misinformation? A Systematic Analysis of the Ad-related Profit Routines of Fake News sites](https://mlsanity.com/paper/2202.05079/) (2022)
- [Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web](https://mlsanity.com/paper/1905.04260/) (2019)
- [And That's A Fact: Distinguishing Factual and Emotional Argumentation in Online Dialogue](https://mlsanity.com/paper/1709.05295/) (2017)
- [Filter List Generation for Underserved Regions](https://mlsanity.com/paper/1910.07303/) (2019)
- [The Rise and Fall of Fake News sites: A Traffic Analysis](https://mlsanity.com/paper/2103.09258/) (2021)
- [Leveraging Google's Publisher-specific IDs to Detect Website Administration](https://mlsanity.com/paper/2202.05074/) (2022)

## Built on by (most cited first, 1 of 1)

- [XSub: Explanation-Driven Adversarial Attack against Blackbox Classifiers via Feature Substitution](https://mlsanity.com/paper/2409.08919/) (2024)

## Abstract (arXiv)

Automatic fake news detection is a challenging problem in misinformation spreading, and it has tremendous real-world political and social impacts. Past studies have proposed machine learning-based methods for detecting such fake news, focusing on different properties of the published news articles, such as linguistic characteristics of the actual content, which however have limitations due to the apparent language barriers. Departing from such efforts, we propose Fake News Detection-as-a Service (FNDaaS), the first automatic, content-agnostic fake news detection method, that considers new and unstudied features such as network and structural characteristics per news website. This method can be enforced as-a-Service, either at the ISP-side for easier scalability and maintenance, or user-side for better end-user privacy. We demonstrate the efficacy of our method using more than 340K datapoints crawled from existing lists of 637 fake and 1183 real news websites, and by building and testing a proof of concept system that materializes our proposal. Our analysis of data collected from these websites shows that the vast majority of fake news domains are very young and appear to have lower time periods of an IP associated with their domain than real news ones. By conducting various experiments with machine learning classifiers, we demonstrate that FNDaaS can achieve an AUC score of up to 0.967 on past sites, and up to 77-92% accuracy on newly-flagged ones.