Stance Reasoner: Zero-Shot Stance Detection on Social Media with
  Explicit Reasoning
Maksym Taranukhin Vered Shwartz Evangelos Milios
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.