Automatic Inference of Brazilian Websites' Reliability for Combating Fake News: Domain and Geolocation Features

Evaluating the reliability of websites that propagate news is critical in combating disinformation. Websites with low reliability often serve as the breeding ground for fake news that spreads rapidly across social networks. In response, this paper introduces an automatic evaluation approach to assessing the reliability of Brazilian websites by analyzing network-related features, eliminating the need for exhaustive content scanning.Unlike previous methodologies focused on social network analysis, our approach leverages publicly available website features, including domain-related features, geolocation data, and TLS certificate attributes. The paper proposes a supervised learning model and curates a comprehensive dataset comprising reliable and unreliable sites. Through rigorous training and evaluation using disjoint data, the model achieves an accuracy greater than 75%, effectively pinpointing reliable content websites.