Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web

TL;DR

This paper presents a new model for classifying websites by credibility, using automatically extracted reputation cues, addressing the lack of accessible trustworthiness indicators, and outperforming existing methods in accuracy.

Contribution

It introduces an automated approach to assess website credibility by leveraging source reputation cues, overcoming limitations of existing manual and proprietary indicators.

Findings

Outperforms state-of-the-art in 2- and 5-classes credibility classification

Provides scalable and automatic credibility scoring for online sources

Helps identify trustworthy versus dubious websites effectively

Abstract

With the growth of the internet, the number of fake-news online has been proliferating every year. The consequences of such phenomena are manifold, ranging from lousy decision-making process to bullying and violence episodes. Therefore, fact-checking algorithms became a valuable asset. To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source. However, most of the widely used Web indicators have either been shut-down to the public (e.g., Google PageRank) or are not free for use (Alexa Rank). Further existing databases are short-manually curated lists of online sources, which do not scale. Finally, most of the research on the topic is theoretical-based or explore confidential data in a restricted simulation environment. In this paper we explore current research, highlight the challenges and propose solutions to tackle the problem of classifying websites into a credibility scale. The proposed model automatically extracts source reputation cues and computes a credibility factor, providing valuable insights which can help in belittling dubious and confirming trustful unknown websites. Experimental results outperform state of the art in the 2-classes and 5-classes setting.