  One very common type of fake news is satire which comes in a form of a news
website or an online platform that parodies reputable real news agencies to
create a sarcastic version of reality. This type of fake news is often
disseminated by individuals on their online platforms as it has a much stronger
effect in delivering criticism than through a straightforward message. However,
when the satirical text is disseminated via social media without mention of its
source, it can be mistaken for real news. This study conducts several
exploratory analyses to identify the linguistic properties of Arabic fake news
with satirical content. We exploit these features to build a number of machine
learning models capable of identifying satirical fake news with an accuracy of
up to 98.6%.
