Abstract
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.
Abstract (translated)
业务沟通的数字化重新组织了说服性话语的过程，这不仅增加了透明度，也使得高级欺骗成为可能。这项研究综合了古典修辞学、传播心理学与语言理论以及金融报告、可持续发展话语和数字营销领域的实证研究，解释了如何使用具有说服力的语言词典系统地检测欺诈性语言。在受控环境中，通过计算文本分析和个人化变压器模型实现了超过99%的检测准确性。然而，在多语种环境下再现这一性能也存在问题，这主要是因为难以找到足够的数据，以及缺乏有效的多语言文本处理基础设施所致。这些证据表明，沟通的理论表示与实证近似之间存在日益扩大的差距，因此有必要建立强大的自动文本识别系统，特别是在基于AI的话语越来越接近于人类交流的情况下。
URL
https://arxiv.org/abs/2508.09935
