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.