Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation

TL;DR

This paper introduces a novel, computationally efficient method for detecting satirical news by leveraging differences in language model prediction losses trained on true and satirical news, enhancing accuracy in distinguishing deceptive content.

Contribution

The paper proposes a new approach that uses language model prediction loss differences to effectively differentiate satirical news from true news, demonstrating improved detection capabilities.

Findings

The method effectively captures language differences between satirical and true news.

It is computationally efficient and sensitive to domain differences.

Experimental results show improved accuracy over baseline methods.

Abstract

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.