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

• URL: http://arxiv.org/abs/2410.08900v2
• Date: Fri, 15 Nov 2024 23:18:53 GMT
• Title: A Benchmark for Cross-Domain Argumentative Stance Classification on Social Media
• Authors: Jiaqing Yuan, Ruijie Xi, Munindar P. Singh,
• Abstract summary: Argumentative stance classification plays a key role in identifying authors' viewpoints on specific topics. Existing benchmarks often come from a single domain or focus on a limited set of topics. We propose leveraging platform rules, readily available expert-curated content, and large language models to bypass the need for human annotation.
• Score: 12.479554210753664
• License: http://creativecommons.org/licenses/by/4.0/
• 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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