Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. However, generating diverse pairs of argumentative sentences across various domains is challenging. Existing benchmarks often come from a single domain or focus on a limited set of topics. Additionally, manual annotation for accurate labeling is time-consuming and labor-intensive. To address these challenges, we propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation. Our approach produces a multidomain benchmark comprising 4,498 topical claims and 30,961 arguments from three sources, spanning 21 domains. We benchmark the dataset in fully supervised, zero-shot, and few-shot settings, shedding light on the strengths and limitations of different methodologies. We release the dataset and code in this study at hidden for anonymity.
@article{arxiv.2410.08900,
title = {A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media},
author = {Jiaqing Yuan and Ruijie Xi and Munindar P. Singh},
journal= {arXiv preprint arXiv:2410.08900},
year = {2024}
}