Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning
<|reference_start|>Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning: 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.<|reference_end|>
@article{taranukhin2024stance,
    title={Stance Reasoner: Zero-Shot Stance Detection on Social Media with
  Explicit Reasoning},
    author={Maksym Taranukhin, Vered Shwartz, Evangelos Milios},
    journal={arXiv preprint arXiv:2403.14895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14895},
    primaryClass={cs.CL cs.AI}
}