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…
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