Automatic author attribution of different languages: A review - Medhi, Smriti Priya; Sarma, Shikhar Kumar (Edelweiss Applied Science and Technology, 2025)
This paper presents a comprehensive analysis of the evolution of writing styles over a century and its implications for author identification. We analyze a diverse corpus of texts spanning a hundred years, focusing on linguistic features such as vocabulary, syntax, and discourse patterns. Our findings reveal significant shifts in writing styles over time, influenced by cultural, technological, and social factors. Furthermore, we investigate the impact of these changes on the task of author identification, a crucial area in forensic linguistics and stylometry. We demonstrate how traditional methods of authorship attribution may be challenged by the evolving nature of language use. However, we also highlight the potential for machine learning techniques, such as deep learning models, to adapt to these changes and improve author identification accuracy. Overall, this study sheds light on the dynamic nature of writing styles and the challenges and opportunities they present for author identification in the digital age, with special importance to low-resource languages.