How humans and machines identify discourse topics: a methodological triangulation

Gillings, M. and Jaworska, S. ORCID: https://orcid.org/0000-0001-7465-2245 (2025) How humans and machines identify discourse topics: a methodological triangulation. Applied Corpus Linguistics, 5 (1). 100121. ISSN 2666-7991 doi: 10.1016/j.acorp.2025.100121

Abstract/Summary

Identifying and exploring discursive topics in texts is of interest to not only linguists, but to researchers working across the full breadth of the social sciences. This paper reports on an exploratory study assessing the influence that analytical method has on the identification and labelling of topics, which might lead to varying interpretations of texts. Using a corpus of corporate sustainability reports, totalling 98,277 words, we asked 6 different researchers to interrogate the corpus and decide on its main ‘topics’ via four different methods: LLM-assisted analyses; topic modelling; concordance analysis; and close reading. These methods differ according to the amount of data that can be analysed at once, the amount of textual context available to the researcher, and the focus of the analysis (i.e., micro to macro). The paper explores how the identified topics differed both between analysts using the same method, and between methods. We conclude with a series of tentative observations regarding the benefits and limitations of each method, and offer recommendations for researchers in choosing which analytical technique to select.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/120575
Identification Number/DOI	10.1016/j.acorp.2025.100121
Refereed	Yes
Divisions	Arts, Humanities and Social Science > School of Literature and Languages > English Language and Applied Linguistics
Uncontrolled Keywords	triangulation; topic modelling; CADS; concordance analysis; close reading, LLMs, ChatGPT, Claude, AI, Corpus Linguistics, Discourse Analysis
Publisher	Elsevier
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