Authorship Attribution Methods, Challenges, and Future Research Directions: A Comprehensive Survey

Abstract

Over the past few decades, researchers have put their effort and paid significant attention to the authorship attribution field, as it plays an important role in software forensics analysis, plagiarism detection, security attack detection, and protection of trade secrets, patent claims, copyright infringement, or cases of software theft. It helps new researchers understand the state-of-the-art works on authorship attribution methods, identify and examine the emerging methods for authorship attribution, and discuss their key concepts, associated challenges, and potential future work that could help newcomers in this field. This paper comprehensively surveys authorship attribution methods and their key classifications, used feature types, available datasets, model evaluation criteria and metrics, and challenges and limitations. In addition, we discuss the potential future research directions of the authorship attribution field based on the insights and lessons learned from this survey work.

Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation

“…The approaches typically refer to statistics or artificial intelligence [34]. In the former case, there could be constructed matrices reflecting probabilities of transitions between certain characters based on a text or texts, which means building a certain limited language model.…”

Authorship Attribution in Less-Resourced Languages: A Hybrid Transformer Approach for Romanian

“…In their recent research, He et al [35] provided a comprehensive examination of the methods, models, datasets, feature types, and evaluation metrics employed in author attribution studies conducted for both source code and English text. The survey included two deep learning studies [36,37] focused on source code author attribution.…”

“…Other CNN-based approaches demonstrated elevated performance on short texts [39][40][41][42][43][44]. The survey [35] also outlined several challenges and constraints. Such limitations for deep learning techniques include considerations of the number of authors, i.e., the higher the variety of authors, the lower the accuracy.…”