METAPHORICAL FRAMINGS IN NEWSPAPER ARTICLES GENERATED BY CHATGPT-5.6 AND GEMINI, DEALING WITH THE WAR IN IRAN
DOI:
https://doi.org/10.35120/sciencej0503349fKeywords:
metaphorical expression, framing, MIPVU, LLM discourseAbstract
The paper aims to explore the range of metaphorical framings in newspapers articles generated by ChatGPT-5.6 and Gemini, dealing with the war in Iran. We have compiled a small specialized corpus with 42 articles and the total corpus length of 19,758 words. LLMs were given topically related article titles and introductory paragraphs chosen randomly from various online sources (e.g., Al Jazeera, Reuters). Based on the supplied information, the LLMs were instructed to generate three different versions of the article, from the following three viewpoints: (i) neutral, (ii) pro-Iranian, and (iii) pro-Israeli. All articles were tagged manually for instances of metaphorically used words, and prepared for subsequent analyses using WordSmith Tools 6.0. Metaphor identification was conducted using the MIPVU (Steen et al., 2010). Quantitative analysis showed a significant main effect of LLMs in the neutral condition (p=.01), with a significantly higher overall mean density of metaphorically used words in articles generated by Gemini. The difference in metaphor density in the two biased-framing conditions did not reach significance. Qualitative analysis revealed differences in the range and types of metaphorical framings between the two LLMs, where the discourse generated by Gemini showed more affective components compared to articles generated by ChatGPT-5.6. The discourse generated by ChatGPT-5.6 was dominated by containment, force, and journey metaphors. While these metaphor groups were also quite frequent in the discourse generated by Gemini, the latter LLM also generated metaphorical expressions corresponding to fire, hydra, cancer, disease, and snake metaphors. Through training and fine-grained instructions, the researched LLMs can pose as useful tools for researching possible framings and their rhetorical functions in discourse.
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References
Breazu, P., & Katsos, N. (2024). ChatGPT-4 as a journalist: Whose perspectives is it reproducing? Discourse & Society, 35(6), 687–707. https://doi.org/10.1177/09579265241251479
Charteris-Black, J. (2004). Corpus Approaches to Critical Metaphor Analysis. Palgrave Macmillan.
Dӧnmez, E., Maurer, M., Lapesa, G., & Falenska, A. (2025). AI Argues Differently: Distinct Argumentative and Linguistic Patterns of LLMs in Persuasive Contexts. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (pp. 34595–346). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.emnlp-main.1755
Figar, V. (2020). Testing the Activation of Semantic Frames in a Lexical Decision and a Categorization Task. Facta Universitatis, Series: Linguistics and Literature, 18(2), 159–179. https://doi.org/10.22190/FULL2002159F
Figar, V. (2021). Semantic Frame Activation and Contextual Aptness of Metaphorical Expressions. Unpublished doctoral dissertation. Faculty of Philosophy, University of Niš, Serbia.
Ichien, N., Stamenković, D., & Holyoak, K. (2024). Large Language Model Displays Emergent Ability to Interpret Novel Literary Metaphors. Metaphor and Symbol, 39(4), 296–309. https://doi.org/10.1080/10926488.2024.2380348
Kimmel, M. (2010). Why we mix metaphors (and mix them well): Discourse coherence, conceptual metaphor, and beyond. Journal of Pragmatics, 42(1), 97–115. https://doi.org/10.1016/j.pragma.2009.05.017
Koester, A. (2010). Building small specialized corpora. In A. O’Keeffe and M. McCarthy (Eds.), The Routledge Handbook of Corpus Linguistics (pp. 66–79). Routledge. https://doi.org/10.4324/9780203856949
Lakoff, G., & Johnson, M. (2003[1980]). Metaphors We Live By. The University of Chicago Press.
Liu, E., Cui, C., Zheng, K., & Neubig, G. (2022). Testing the ability of language models to interpret figurative language. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4437–4452). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.naacl-main.330
Muñoz-Ortiz, A., Gómez‑Rodríguez, C., & Vilares, D. (2024). Contrasting Linguistic Patterns in Human and LLM‑Generated News Text. Artificial Intelligence Review, 57(265), 1–28. https://doi.org/10.1007/s10462-024-10903-2
Scott, M. (2010). What can corpus software do? In A. O’Keeffe and M. McCarthy (Eds.), The Routledge Handbook of Corpus Linguistics (pp. 136–151). Routledge. https://doi.org/10.4324/9780203856949
Steen, G. J., Dorts, A. G., Herrmann, J. B., Kaal, A. A., Krennmayr, T., & Pasma, T. (2010). A Method for Linguistic Metaphor Identification: From MIP to MIPVU. John Benjamins Publishing Company. https://doi.org/10.1075/celcr.14
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