@inproceedings{taranukhin-etal-2024-stance,
    title = "Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning",
    author = "Taranukhin, Maksym  and
      Shwartz, Vered  and
      Milios, Evangelos",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1326",
    pages = "15257--15272",
    abstract = "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.",
}

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