FNDaaS: Content-agnostic Detection of Fake News sites

TL;DR

This paper introduces FNDaaS, a novel content-agnostic fake news detection system leveraging network and structural features, demonstrating high accuracy and scalability for identifying fake news websites.

Contribution

It presents the first content-agnostic fake news detection method that uses network and structural features, enabling scalable detection as a service at ISP or user level.

Findings

Fake news domains are typically young and have shorter IP association periods.

FNDaaS achieves up to 0.967 AUC score on past sites.

FNDaaS attains 77-92% accuracy on newly-flagged fake news sites.

Abstract

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.