Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
This addresses the problem of automatically extracting user opinions on new topics from social media content, offering an interpretable solution with strong generalization.
The paper tackled zero-shot stance detection on social media by developing Stance Reasoner, which uses explicit reasoning over background knowledge, and it outperformed state-of-the-art models on three Twitter datasets, including supervised ones.
Social media platforms are rich sources of opinionated content. Stance detection allows the automatic extraction of users' opinions on various topics from such content. We focus on zero-shot stance detection, where the model's success relies on (a) having knowledge about the target topic; and (b) learning general reasoning strategies that can be employed for new topics. We present Stance Reasoner, an approach to zero-shot stance detection on social media that leverages explicit reasoning over background knowledge to guide the model's inference about the document's stance on a target. Specifically, our method uses a pre-trained language model as a source of world knowledge, with the chain-of-thought in-context learning approach to generate intermediate reasoning steps. Stance Reasoner outperforms the current state-of-the-art models on 3 Twitter datasets, including fully supervised models. It can better generalize across targets, while at the same time providing explicit and interpretable explanations for its predictions.