# Adversarial Training for Satire Detection: Controlling for Confounding Variables

## TL;DR

This paper introduces an adversarial training approach for satire detection that reduces reliance on publication source cues, leading to more linguistically focused and generalizable models.

## Contribution

It proposes a novel adversarial model that controls for publication source confounding variables in satire detection, improving linguistic focus and generalization.

## Key findings

- Comparable satire classification performance with and without adversarial training
- Significant reduction in publication source classification when using adversarial training
- Adversarial component enhances the model's focus on satire's linguistic features

## Abstract

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.