用语言分析技术识别商业沟通中的说服与欺骗话术
Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach
- 结合修辞学与语言理论，构建欺骗性话语检测框架
- 在可控环境下实现超99%的检测准确率
- 适合关注AI沟通伦理、金融信息披露的研究者
商业沟通的数字化重塑了说服性话语的生成方式，既提升了透明度，也加剧了隐蔽欺骗。本文融合经典修辞学、传播心理学、语言学理论及财务报告、可持续发展话语与数字营销的实证研究，提出基于说服性词汇库的欺骗语言系统检测方法。在受控环境中，通过计算文本分析与个性化Transformer模型，检测准确率超过99%。然而，在多语言场景下复现此性能仍具挑战，主要因数据稀缺且缺乏多语言文本处理基础设施。证据表明，沟通的理论表征与实证近似之间差距日益扩大，因此亟需强大的自动文本识别系统，以应对日益逼真的AI人际交流。
原文摘要 · Abstract (English)
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.
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