A Multi-Modal Method for Satire Detection using Textual and Visual Cues
Published in
(2020)External Links:
TL
TL;DR
This work creates a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection, and proposes a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT.
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.
References21 items
1
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
2
Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
3
ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks
4
Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model
5