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Adversarial Learning for Zero-Shot Stance Detection on Social Media
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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.
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Cited by 1 Pith paper
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A More Advanced Group Polarization Measurement Approach Based on LLM-Based Agents and Graphs
Group polarization is measured through a Community Sentiment Network built by a team of LLM agents, with a Community Opposition Index score; only the stance detection step is empirically tested.
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