  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.
