@inproceedings{giovannetti-cristiano-2026-exploring,
title = "Exploring the Use of Large Language Models in Critical Discourse Analysis: A Consensus-Based Pilot Study",
author = "Giovannetti, Emiliano and
Cristiano, Francesca",
editor = "Montejo-Raez, Arturo and
Grisot, Cristina and
Blochowiak, Joanna and
Ljube{\v{s}}i{\'c}, Nikola and
Battaner, Elena and
Rigau, German",
booktitle = "Proceedings of Shaping Multilingual, Multimodal {AI} for the Social Sciences and Humanities ({LLM}s4{SSH}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma de Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.llms4ssh-1.2/",
doi = "10.63317/46h2k27ufevn",
pages = "18--22",
abstract = "Large Language Models (LLMs) are increasingly used in the social sciences and humanities (SSH) to support the analysis of complex textual data, raising methodological questions about evaluation and interpretive reliability. This paper explores the use of LLMs in Critical Discourse Analysis (CDA), considered here as a paradigmatic case of interpretive research in SSH, through a preliminary consensus-based evaluation framework. The study reports on a pilot experiment conducted on a small, theory-driven corpus of opinion articles addressing the October 7, 2023 attack and its aftermath. An LLM is asked to answer analytically motivated questions targeting different levels of discourse structure. Its responses are compared with annotations produced by multiple human analysts and aggregated through a consensus-based procedure. The results reveal an asymmetry in model performance: while LLMs align well with human consensus on macro- and superstructural features, they struggle with microstructural phenomena involving implicit meaning. These findings support the view of LLMs as epistemic support tools rather than replacements for human interpretation."
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<abstract>Large Language Models (LLMs) are increasingly used in the social sciences and humanities (SSH) to support the analysis of complex textual data, raising methodological questions about evaluation and interpretive reliability. This paper explores the use of LLMs in Critical Discourse Analysis (CDA), considered here as a paradigmatic case of interpretive research in SSH, through a preliminary consensus-based evaluation framework. The study reports on a pilot experiment conducted on a small, theory-driven corpus of opinion articles addressing the October 7, 2023 attack and its aftermath. An LLM is asked to answer analytically motivated questions targeting different levels of discourse structure. Its responses are compared with annotations produced by multiple human analysts and aggregated through a consensus-based procedure. The results reveal an asymmetry in model performance: while LLMs align well with human consensus on macro- and superstructural features, they struggle with microstructural phenomena involving implicit meaning. These findings support the view of LLMs as epistemic support tools rather than replacements for human interpretation.</abstract>
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%0 Conference Proceedings
%T Exploring the Use of Large Language Models in Critical Discourse Analysis: A Consensus-Based Pilot Study
%A Giovannetti, Emiliano
%A Cristiano, Francesca
%Y Montejo-Raez, Arturo
%Y Grisot, Cristina
%Y Blochowiak, Joanna
%Y Ljubešić, Nikola
%Y Battaner, Elena
%Y Rigau, German
%S Proceedings of Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities (LLMs4SSH) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma de Mallorca (Spain)
%F giovannetti-cristiano-2026-exploring
%X Large Language Models (LLMs) are increasingly used in the social sciences and humanities (SSH) to support the analysis of complex textual data, raising methodological questions about evaluation and interpretive reliability. This paper explores the use of LLMs in Critical Discourse Analysis (CDA), considered here as a paradigmatic case of interpretive research in SSH, through a preliminary consensus-based evaluation framework. The study reports on a pilot experiment conducted on a small, theory-driven corpus of opinion articles addressing the October 7, 2023 attack and its aftermath. An LLM is asked to answer analytically motivated questions targeting different levels of discourse structure. Its responses are compared with annotations produced by multiple human analysts and aggregated through a consensus-based procedure. The results reveal an asymmetry in model performance: while LLMs align well with human consensus on macro- and superstructural features, they struggle with microstructural phenomena involving implicit meaning. These findings support the view of LLMs as epistemic support tools rather than replacements for human interpretation.
%R 10.63317/46h2k27ufevn
%U https://aclanthology.org/2026.llms4ssh-1.2/
%U https://doi.org/10.63317/46h2k27ufevn
%P 18-22
Markdown (Informal)
[Exploring the Use of Large Language Models in Critical Discourse Analysis: A Consensus-Based Pilot Study](https://aclanthology.org/2026.llms4ssh-1.2/) (Giovannetti & Cristiano, LLMs4SSH 2026)
ACL