  Satirical news detection is an important yet challenging task to prevent
spread of misinformation. Many feature based and end-to-end neural nets based
satirical news detection systems have been proposed and delivered promising
results. Existing approaches explore comprehensive word features from satirical
news articles, but lack semantic metrics using word vectors for tweet form
satirical news. Moreover, the vagueness of satire and news parody determines
that a news tweet can hardly be classified with a binary decision, that is,
satirical or legitimate. To address these issues, we collect satirical and
legitimate news tweets, and propose a semantic feature based approach. Features
are extracted by exploring inconsistencies in phrases, entities, and between
main and relative clauses. We apply game-theoretic rough set model to detect
satirical news, in which probabilistic thresholds are derived by game
equilibrium and repetition learning mechanism. Experimental results on the
collected dataset show the robustness and improvement of the proposed approach
compared with Pawlak rough set model and SVM.
