JOURNAL ARTICLE

The analysis of argumentation topoi: A qualitative approach goes to large corpora.

  • Published In: Nota Bene: Journal for Linguistics in Belgium & the Netherlands, 2025, v. 2, n. 1. P. 106 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kiemes, Carina; Müller, Marcus; Wengeler, Martin 3 of 3

Abstract

This paper examines the role of argumentation topoi in discourse analysis, focusing on their identification, annotation, and automated classification. While topos analysis is a well-established method for uncovering collective patterns of reasoning in public discourse, its application in large-scale digital discourse analysis poses methodological challenges. This study investigates the so-called utility topos in bioethical debates, which encompasses both medical and economic arguments about benefit. We collaboratively developed annotation guidelines to ensure consistency and measured inter-annotator agreement. We then trained a BERT-based model on the gold-standard annotations and used it to classify utility topoi in German texts. The results indicate that while explicit argument structures are detectable, implicit and context-dependent reasoning remains difficult to capture. These findings highlight the need for refined annotation guidelines and contextual modelling in automated discourse analysis. The study contributes to the integration of qualitative and computational methods in argument analysis. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Nota Bene: Journal for Linguistics in Belgium & the Netherlands. 2025/01, Vol. 2, Issue 1, p106
  • Document Type:Article
  • Subject Area:Communication and Mass Media
  • Publication Date:2025
  • ISSN:2950-189X
  • DOI:10.1075/nb.00025.kie
  • Accession Number:188296995
  • Copyright Statement:Copyright of Nota Bene: Journal for Linguistics in Belgium & the Netherlands is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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