Abstract: 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.

Muhammad Irfaan Hossen Rujeedawa, Sameerchand Pudaruth and Vusumuzi Malele, “Unmasking AI-Generated Texts Using Linguistic and Stylistic Features” International Journal of Advanced Computer Science and Applications(IJACSA), 16(3), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160321

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