Authorship Analysis

Authorship analysis aims to identify or infer characteristics of a text's author, encompassing tasks like authorship attribution, verification, and profiling. Current research heavily utilizes large language models (LLMs) and deep learning architectures like transformers, often incorporating techniques like style transfer and feature extraction from parsed language structures to improve accuracy and interpretability. This field is crucial for addressing concerns around plagiarism, misinformation, and online privacy, with applications ranging from forensic linguistics to the detection of AI-generated content and the study of evolving writing styles.