Irony and artificial intelligence: linguistic-discursive strategies in the production of automated humor

Authors

DOI:

https://doi.org/10.11606/issn.2236-4242.v39i1p174-194

Keywords:

Ironic humor, Generative Artificial Intelligence (GAI), Discourse analysis

Abstract

This study investigates the production of ironic humor by Generative Artificial Intelligences (GAIs), focusing on the models ChatGPT and DeepSeek. Through a qualitative-interpretative analysis of texts generated from a simple prompt, “Write an ironic humorous text,” the research combines humor theories and critical discourse analysis to examine the linguistic-discursive strategies employed by GAIs in constructing humor. The results indicate that both GAIs go beyond merely reproducing linguistic patterns, incorporating creative elements such as hyperbole, metalanguage, and paradox to build complex irony that engages with contemporary themes such as consumption, technology, productivity, and digital culture. It is observed that ChatGPT adopts a more colloquial and accessible language, while DeepSeek produces more sophisticated and reflective texts, requiring greater interpretive effort from the reader. The study also highlights the interdependence between human intervention through prompts and automated text generation, as well as the ethical challenges related to the reproduction of biases in the generated content. Finally, this research contributes to broadening the understanding of the role of GAIs in cultural creation and in the construction of complex meanings.

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Author Biography

  • Anielle Andrade de Sousa, Bahia State University

    Doutoranda em Crítica Cultural pela Universidade do Estado da Bahia, Brasil.

References

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Published

2026-04-30

Issue

Vol. 39 No. 1 (2026): Digital Discourse – Contemporary Challenges and Problems

Section

Articles

License

Copyright (c) 2026 Anielle Andrade de Sousa

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How to Cite

SOUSA, Anielle Andrade de. Irony and artificial intelligence: linguistic-discursive strategies in the production of automated humor. Linha D’Água, São Paulo, v. 39, n. 1, p. 174–194, 2026. DOI: 10.11606/issn.2236-4242.v39i1p174-194. Disponível em: https://revistas.usp.br/linhadagua/article/view/240761. Acesso em: 3 nov. 2026.

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