Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features

TL;DR

This paper proposes a novel approach for satirical news detection that leverages paragraph-level linguistic features combined with attention mechanisms, outperforming previous document-level methods and providing insights into key satirical cues.

Contribution

It introduces a paragraph-level feature analysis with attention mechanisms for satire detection, addressing limitations of prior document-level approaches.

Findings

The proposed model effectively detects satirical news.

Paragraph-level features reveal more nuanced satirical cues.

Attention mechanisms identify important paragraphs and features.

Abstract

Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news. We observe that satirical cues are often reflected in certain paragraphs rather than the whole document. Existing works only consider document-level features to detect the satire, which could be limited. We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism. We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset. The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.