{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stance-reasoner-zero-shot-stance-detection-on","title":"Stance Reasoner: Zero-Shot Stance Detection on Social Media with Explicit Reasoning","arxiv_id":"2403.14895","date":"2024-03-22","proceeding":null,"authors":["Maksym Taranukhin","Vered Shwartz","Evangelos Milios"],"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.","url_abs":"https://arxiv.org/abs/2403.14895v1","url_pdf":"https://arxiv.org/pdf/2403.14895v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stance-reasoner-zero-shot-stance-detection-on","repo_url":"https://github.com/maksym-taranukhin/stance_reasoner","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"few-shot-stance-detection","task_name":"Few-Shot Stance Detection"},{"task_slug":"in-context-learning","task_name":"In-Context Learning"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"stance-detection","task_name":"Stance Detection"},{"task_slug":"world-knowledge","task_name":"World Knowledge"},{"task_slug":"zero-shot-stance-detection","task_name":"Zero-Shot Stance Detection"}],"methods":[{"method_slug":"cot-prompting","method_name":"CoT Prompting"},{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}