A Multi-Modal Method for Satire Detection using Textual and Visual Cues

• URL: http://arxiv.org/abs/2010.06671v1
• Date: Tue, 13 Oct 2020 20:08:29 GMT
• Title: A Multi-Modal Method for Satire Detection using Textual and Visual Cues
• Authors: Lily Li, Or Levi, Pedram Hosseini, David A. Broniatowski
• Abstract summary: Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news. We observe that the images used in satirical news articles often contain absurd or ridiculous content. We propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT.
• Score: 5.147194328754225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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