Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches

TL;DR

This review paper systematically examines the evolution, methodologies, and challenges of authorship analysis, focusing on ML, DL, and LLM approaches from 2015 to 2024, highlighting research gaps and future directions.

Contribution

It provides a comprehensive analysis of authorship analysis methods, datasets, and challenges, emphasizing emerging issues like low-resource languages and AI-generated text detection.

Findings

Identification of key trends in ML, DL, and LLM approaches

Highlighting research gaps in multilingual and low-resource contexts

Discussion of challenges in cross-domain generalization and AI-generated text detection

Abstract

Authorship analysis plays an important role in diverse domains, including forensic linguistics, academia, cybersecurity, and digital content authentication. This paper presents a systematic literature review on two key sub-tasks of authorship analysis; Author Attribution and Author Verification. The review explores SOTA methodologies, ranging from traditional ML approaches to DL models and LLMs, highlighting their evolution, strengths, and limitations, based on studies conducted from 2015 to 2024. Key contributions include a comprehensive analysis of methods, techniques, their corresponding feature extraction techniques, datasets used, and emerging challenges in authorship analysis. The study highlights critical research gaps, particularly in low-resource language processing, multilingual adaptation, cross-domain generalization, and AI-generated text detection. This review aims to help researchers by giving an overview of the latest trends and challenges in authorship analysis. It also points out possible areas for future study. The goal is to support the development of better, more reliable, and accurate authorship analysis system in diverse textual domain.