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

arXiv: https://arxiv.org/abs/2010.06671 · HTML: https://mlsanity.com/paper/2010.06671/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [End-to-End Multimodal Fact-Checking and Explanation Generation: A Challenging Dataset and Models](https://mlsanity.com/paper/2205.12487/) (2022)
- [Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities](https://mlsanity.com/paper/2203.13883/) (2022)
- [SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles](https://mlsanity.com/paper/2105.06456/) (2021)
- [YesBut: A High-Quality Annotated Multimodal Dataset for evaluating Satire Comprehension capability of Vision-Language Models](https://mlsanity.com/paper/2409.13592/) (2024)
- [FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection](https://mlsanity.com/paper/2104.04828/) (2021)
- [Never guess what I heard... Rumor Detection in Finnish News: a Dataset and a Baseline](https://mlsanity.com/paper/2106.03389/) (2021)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [POLygraph: Polish Fake News Dataset](https://mlsanity.com/paper/2407.01393/) (2024)
- [Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English](https://mlsanity.com/paper/2411.10730/) (2024)
- [Unreliable Narrator Framing as a Cost-effective Mechanism for Satire Explanation of Australian and American Satirical News](https://mlsanity.com/paper/2610.12863/) (2026)

## Abstract (arXiv)

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.