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  • Arxiv:2010.06671V1 [Cs.CL] 13 Oct 2020
    A Multi-Modal Method for Satire Detection using Textual and Visual Cues Lily Li1, Or Levi2, Pedram Hosseini3, David A. Broniatowski3 1Jericho Senior High School, New York, USA 2AdVerifai, Amsterdam, Netherlands 3The George Washington University, Washington D.C., USA [email protected], [email protected] fphosseini,[email protected] Abstract Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While previous work have studied text-based methods, in this work we propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT. To this end, we create a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection. We fine-tune ViLBERT on the dataset and train a convolutional neural network that uses an image forensics technique. Evaluation on the dataset shows that our proposed multi-modal approach outperforms image-only, text-only, and simple fusion baselines. 1 Introduction Satire is a literary device that writers employ to mock or ridicule a person, group, or ideology by passing judgment on them for a cultural transgression or poor social behavior. Satirical news utilizes humor and irony by placing the target of the criticism into a ridiculous, fictional situation that the reader must suspend their disbelief and go along with (Maslo, 2019). However, despite what absurd content satirical news may contain, it is often mistaken by readers as real, legitimate news, which may then lead to the unintentional spread of misinformation.
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  • Preprint Brugman & Burgers (In Press) Sounds Like a Funny Joke
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  • Don't Be Fooled by a Fake
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