Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach
This work addresses the need for automated text-identification systems to combat deception in AI-driven business discourse, though it is incremental in applying existing methods to new data.
The paper tackled the problem of detecting deceptive language in business communication by synthesizing rhetoric, psychology, and linguistics with computational methods, achieving detection accuracies over 99% in controlled settings. However, it faced challenges in multilingual reproducibility due to data and infrastructure limitations.
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.