  Satirical news is regularly shared in modern social media because it is
entertaining with smartly embedded humor. However, it can be harmful to society
because it can sometimes be mistaken as factual news, due to its deceptive
character. We found that in satirical news, the lexical and pragmatical
attributes of the context are the key factors in amusing the readers. In this
work, we propose a method that differentiates the satirical news and true news.
It takes advantage of satirical writing evidence by leveraging the difference
between the prediction loss of two language models, one trained on true news
and the other on satirical news, when given a new news article. We compute
several statistical metrics of language model prediction loss as features,
which are then used to conduct downstream classification. The proposed method
is computationally effective because the language models capture the language
usage differences between satirical news documents and traditional news
documents, and are sensitive when applied to documents outside their domains.
