  In early January 2020, after China reported the first cases of the new
coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully
accurate information has started spreading faster than the virus itself.
Alongside this pandemic, people have experienced a parallel infodemic, i.e., an
overabundance of information, some of which misleading or even harmful, that
has widely spread around the globe. Although Social Media are increasingly
being used as information source, Web Search Engines, like Google or Yahoo!,
still represent a powerful and trustworthy resource for finding information on
the Web. This is due to their capability to capture the largest amount of
information, helping users quickly identify the most relevant, useful, although
not always the most reliable, results for their search queries. This study aims
to detect potential misleading and fake contents by capturing and analysing
textual information, which flow through Search Engines. By using a real-world
dataset associated with recent CoViD-19 pandemic, we first apply re-sampling
techniques for class imbalance, then we use existing Machine Learning
algorithms for classification of not reliable news. By extracting lexical and
host-based features of associated Uniform Resource Locators (URLs) for news
articles, we show that the proposed methods, so common in phishing and
malicious URLs detection, can improve the efficiency and performance of
classifiers. Based on these findings, we think that usage of both textual and
URLs features can improve the effectiveness of fake news detection methods.
