  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.
