Stylometric Feature

Stylometric analysis examines the unique linguistic features of written text to identify authorship, detect AI-generated content, or analyze stylistic changes over time. Current research focuses on developing robust models, often employing machine learning algorithms like Random Forests and employing features such as n-grams, part-of-speech tags, and function word frequencies, to distinguish between human and AI writing styles across various languages. These advancements have significant implications for fields like digital forensics, plagiarism detection, literary studies, and the detection of AI-generated misinformation.