# Adversarial Training for Satire Detection: Controlling for Confounding Variables

arXiv: https://arxiv.org/abs/1902.11145 · HTML: https://mlsanity.com/paper/1902.11145/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 13 of 13)

- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection](https://mlsanity.com/paper/2207.08003/) (2022)
- [SaRoCo: Detecting Satire in a Novel Romanian Corpus of News Articles](https://mlsanity.com/paper/2105.06456/) (2021)
- [FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection](https://mlsanity.com/paper/2104.04828/) (2021)
- [Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection](https://mlsanity.com/paper/2104.09261/) (2021)
- [SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification](https://mlsanity.com/paper/2303.09421/) (2023)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [Can Factual Statements be Deceptive? The DeFaBel Corpus of Belief-based Deception](https://mlsanity.com/paper/2403.10185/) (2024)
- [Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification](https://mlsanity.com/paper/2205.07283/) (2022)
- [Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis](https://mlsanity.com/paper/2306.07845/) (2023)
- [Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation](https://mlsanity.com/paper/2309.03238/) (2023)
- [PoPreRo: A New Dataset for Popularity Prediction of Romanian Reddit Posts](https://mlsanity.com/paper/2407.04541/) (2024)
- [Comparison of Multilingual and Bilingual Models for Satirical News Detection of Arabic and English](https://mlsanity.com/paper/2411.10730/) (2024)

## Abstract (arXiv)

The automatic detection of satire vs. regular news is relevant for downstream applications (for instance, knowledge base population) and to improve the understanding of linguistic characteristics of satire. Recent approaches build upon corpora which have been labeled automatically based on article sources. We hypothesize that this encourages the models to learn characteristics for different publication sources (e.g., "The Onion" vs. "The Guardian") rather than characteristics of satire, leading to poor generalization performance to unseen publication sources. We therefore propose a novel model for satire detection with an adversarial component to control for the confounding variable of publication source. On a large novel data set collected from German news (which we make available to the research community), we observe comparable satire classification performance and, as desired, a considerable drop in publication classification performance with adversarial training. Our analysis shows that the adversarial component is crucial for the model to learn to pay attention to linguistic properties of satire.