Unmasking AI-Generated Texts Using Linguistic and Stylistic Features

As Artificial Intelligence (AI) generated texts become increasingly sophisticated, distinguishing between human-written and AI-generated content presents a growing challenge. Reliably detecting AI-generated texts is of primary importance in fields that involve a lot of text such as journalism, education and law. In this study, several methods for detecting AI-generated texts by analysing a range of linguistic and stylistic features were investigated. It incorporated features such as text length, punctuation count, vocabulary richness, readability indices and sentiment polarity, to identify patterns in AI-generated content. Out of the six machine learning classifiers which were tested, the Random Forest classifier achieved the highest accuracy of 82.6%. A dataset of 483,360 essays was used in this study. Thus, the findings of this study provide a framework for the development of more sophisticated detection tools that can be applied to various real-world scenarios.

@article{Rujeedawa2025,
  title     = {Unmasking AI-Generated Texts Using Linguistic and Stylistic Features},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Irfaan Hossen Rujeedawa and Sameerchand Pudaruth and Vusumuzi Malele},
  doi       = {10.14569/IJACSA.2025.0160321},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160321}
}