Developing corpora through data mining for qualitative argumentation research

Musi, E ORCID: 0000-0003-2431-455X and Rocci, A (2025) Developing corpora through data mining for qualitative argumentation research In: Qualitative Research Methods in Argumentation Studies. Taylor & Francis, pp. 58-75.

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Summary

In the networked society, a key challenge in argumentation research is the collection and selection of suitable corpora to investigate phenomena through qualitative approaches. This chapter contributes to that effort by introducing concepts from mixed-methods research into argumentation studies. We propose a two-step decision-making framework for corpus selection. The first step involves assessing three key dimensions—argumentative issue, theoretical framework, and activity type—to identify appropriate qualitative and quantitative data sources. To illustrate these dimensions, we draw on examples from existing argumentation literature. The second step focuses on clarifying the purpose of a mixed-methods approach and selecting the most suitable methodological combination. Additionally, we provide practical guidelines for data collection, with particular attention to digital media and the broader contemporary (mis)information ecosystem. Finally, we explore how advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI) have transformed both corpus construction and the nature and quality of argumentative data.

Item Type:	Chapter
Uncontrolled Keywords:	4605 Data Management and Data Science, 46 Information and Computing Sciences, 47 Language, Communication and Culture, Machine Learning and Artificial Intelligence
Divisions:	Faculty of Humanities & Social Sciences Faculty of Humanities & Social Sciences > School of the Arts Faculty of Humanities & Social Sciences > School of the Arts > Communication and Media Faculty of Humanities & Social Sciences > Faculty of Humanities & Social Sci (All T&R Staff) Faculty of Humanities & Social Sciences > School of the Arts > School of the Arts (T&R Staff)
Depositing User:	Symplectic Admin
Date Deposited:	06 Jan 2026 09:04
Last Modified:	23 May 2026 10:53
DOI:	10.4324/9781003502296-4
Related Websites:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3196431
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