Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909736023097344 |
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| author | Hossen, Sayem Joti, Monalisa Moon Rashed, Md. Golam |
| author_facet | Hossen, Sayem Joti, Monalisa Moon Rashed, Md. Golam |
| contents | Business communication digitisation has reorganised the process of persuasive discourse, which
allows not only greater transparency but also advanced deception. This inquiry synthesises classical
rhetoric and communication psychology with linguistic theory and empirical studies in the financial
reporting, sustainability discourse, and digital marketing to explain how deceptive language can be
systematically detected using persuasive lexicon. In controlled settings, detection accuracies of greater
than 99% were achieved by using computational textual analysis as well as personalised transformer
models. However, reproducing this performance in multilingual settings is also problematic and,
to a large extent, this is because it is not easy to find sufficient data, and because few multilingual
text-processing infrastructures are in place. This evidence shows that there has been an increasing
gap between the theoretical representations of communication and those empirically approximated,
and therefore, there is a need to have strong automatic text-identification systems where AI-based
discourse is becoming more realistic in communicating with humans. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09935 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach Hossen, Sayem Joti, Monalisa Moon Rashed, Md. Golam Computation and Language Computational Finance General Finance Business communication digitisation has reorganised the process of persuasive discourse, which allows not only greater transparency but also advanced deception. This inquiry synthesises classical rhetoric and communication psychology with linguistic theory and empirical studies in the financial reporting, sustainability discourse, and digital marketing to explain how deceptive language can be systematically detected using persuasive lexicon. In controlled settings, detection accuracies of greater than 99% were achieved by using computational textual analysis as well as personalised transformer models. However, reproducing this performance in multilingual settings is also problematic and, to a large extent, this is because it is not easy to find sufficient data, and because few multilingual text-processing infrastructures are in place. This evidence shows that there has been an increasing gap between the theoretical representations of communication and those empirically approximated, and therefore, there is a need to have strong automatic text-identification systems where AI-based discourse is becoming more realistic in communicating with humans. |
| title | Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach |
| topic | Computation and Language Computational Finance General Finance |
| url | https://arxiv.org/abs/2508.09935 |