Automatic Credibility Inference of Websites: Domain Features and Geolocation to Combat Fake News

• Marcos Paulo Cezar de Mendonça UFF
• Igor Monteiro Moraes UFF
• Diogo Menezes Ferrazani Mattos UFF

DOI: https://doi.org/10.5753/wperformance.2024.2722

Abstract

Evaluating the credibility of websites that propagate news is a critical activity in combating disinformation. Websites of low reliability are sometimes pointed out as the origin of fake news propagated and amplified on social networks. This article proposes an automatic evaluation of the credibility of websites, without the need to scan all the site’s content. Unlike previous works focusing on social networks, this article uses publicly available features of websites, such as domain characteristics, geolocation, and TLS certificate, to identify reliable and unreliable websites, using supervised machine learning techniques. The article proposes a supervised learning model and consolidates a dataset of reliable and unreliable sites. The model was trained and evaluated with disjoint data and it was possible to effectively identify, with an accuracy greater than 75%, reliable and unreliable websites, contributing to the fight against the spread of fake news and disinformation.

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