Authorship and Gender Attribution

This chapter offers a comprehensive overview of authorship attribution and gender attribution within the broader traditions of stylometry and linguistic individuality. We trace theoretical foundations and methodological advances from classical stylometric techniques to contemporary machine-learning and large language model (LLM) approaches. Applications spanning literary studies, forensic linguistics, digital humanities, and cybersecurity are surveyed, alongside persistent challenges such as limited text length, cross-genre and cross-topic transfer, temporal drift in style, and intentional style obfuscation. We also examine the intersection of authorship and gender attribution, examining how gendered linguistic patterns interact with author-specific signals. Moreover, we review recent developments, including transformer-based models, transfer learning, and multilingual pipelines. The chapter concludes with ethical considerations (privacy, inclusivity beyond binary classifications, and risks of misuse) and outlines future directions that emphasize cross-linguistic research, interdisciplinary collaboration, and responsible deployment.