Adversarial Learning for Zero-Shot Stance Detection on Social Media

TL;DR

This paper introduces an adversarial learning model for zero-shot stance detection on social media, enabling generalization across unseen topics with minimal computational costs, and explores extending this approach to new topics.

Contribution

The paper presents a novel adversarial learning framework for zero-shot stance detection on Twitter, achieving state-of-the-art results and extending capabilities to new topics.

Findings

Achieves state-of-the-art performance on unseen topics

Uses minimal computational resources

Extends zero-shot detection to new topics

Abstract

Stance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection on Twitter that uses adversarial learning to generalize across topics. Our model achieves state-of-the-art performance on a number of unseen test topics with minimal computational costs. In addition, we extend zero-shot stance detection to new topics, highlighting future directions for zero-shot transfer.