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

Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features