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arXiv · 2410.08900

A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media

Jiaqing Yuan·Ruijie Xi·Munindar P. Singh
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Abstract

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.

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Jiaqing Yuan, Ruijie Xi, Munindar P. Singh. 2024-10-11. A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media. https://arxiv.org/abs/2410.08900

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