Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach

Paper: arXiv 2508.09935

Authors: Sayem Hossen, Monalisa Moon Joti, Md. Golam Rashed

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.

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 4.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper applies computational linguistics and transformer models to detect deceptive language but lacks the mathematical formalism or heavy derivations typical of high-complexity work. While it reports high accuracies, the discussion of data limitations and lack of implementation details place it in a lower empirical rigor bracket, focusing more on theoretical synthesis than backtest-ready methodology.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>How to detect deceptive language in<br>digitised business communication?"] --> B["Methodology: Synthesise & Model"]
  B --> C["Data Sources:<br>Financial Reporting<br>Sustainability Discourse<br>Digital Marketing"]
  C --> D["Computation:<br>Transformer Models &<br>Textual Analysis"]
  D --> E{"Evaluation"}
  E -- Controlled Settings --> F[">99% Accuracy"]
  E -- Multilingual Settings --> G["Data Scarcity &<br>Infrastructure Limits"]
  F & G --> H["Outcome: Theoretical-Practical<br>Gap; Need for Robust AI Systems"]

Cited References (261)

1. Detecting fraudulent behavior on crowdfunding platforms: The role of linguistic and content-based cues in static and dynamic contexts (2016)
2. Detecting deceptive discussions in conference calls (2012)
3. Accounting variables, deception, and a bag of words: Assessing the tools of fraud detection (2012)
4. Following linguistic footprints: Automatic deception detection in online communication (2008)
5. Untangling a web of lies: Exploring automated detection of deception in computer-mediated communication (2016)
6. An exploratory study into deception detection in text-based computer-mediated communication (2003)
7. On lying and being lied to: A linguistic analysis of deception in computer-mediated communication (2008)
8. The effects of communication media and culture on deception detection accuracy (2018)

0 of 261 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.