“Tell me who you are and I tell you how you argue”: Predicting Stances and Arguments for Stakeholder Groups

Philipp Heinisch, Lorik Dumani, Philipp Cimiano, Ralf Schenkel


Abstract
Argument mining has focused so far mainly on the identification, extraction, and formalization of arguments. An important yet unaddressedtask consists in the prediction of the argumentative behavior of stakeholders in a debate. Predicting the argumentative behavior in advance can support foreseeing issues in public policy making or help recognize potential disagreements early on and help to resolve them. In this paper, we consider the novel task of predicting the argumentative behavior of individual stakeholders. We present ARGENST, a framework that relies on a recommender-based architecture to predict the stance and the argumentative main point on a specific controversial topic for a given stakeholder, which is described in terms of a profile including properties related to demographic attributes, religious and political orientation, socio-economic background, etc. We evaluate our approach on the well-known debate.org dataset in terms of accuracy for predicting stance as well as in terms of similarity of the generated arguments to the ground truth arguments using BERTScore. As part of a case study, we show how juries of members representing different stakeholder groups and perspectives can be assembled to simulate the public opinion on a given topic.
Anthology ID:
2024.findings-naacl.128
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1968–1982
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URL:
https://aclanthology.org/2024.findings-naacl.128
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Cite (ACL):
Philipp Heinisch, Lorik Dumani, Philipp Cimiano, and Ralf Schenkel. 2024. “Tell me who you are and I tell you how you argue”: Predicting Stances and Arguments for Stakeholder Groups. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1968–1982, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
“Tell me who you are and I tell you how you argue”: Predicting Stances and Arguments for Stakeholder Groups (Heinisch et al., Findings 2024)
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https://aclanthology.org/2024.findings-naacl.128.pdf
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