Adversarial Learning for Zero-Shot Stance Detection on Social Media
This work addresses stance detection on social media to identify slanted news, but it is incremental as it extends zero-shot methods to new topics.
The paper tackles zero-shot stance detection on Twitter by proposing an adversarial learning model that generalizes across topics, achieving state-of-the-art performance on unseen test topics with minimal computational costs.
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.