# Assessing the Applicability of Authorship Verification Methods

arXiv: https://arxiv.org/abs/1906.10551 · HTML: https://mlsanity.com/paper/1906.10551/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 11 of 11)

- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution](https://mlsanity.com/paper/2110.14203/) (2021)
- [Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification](https://mlsanity.com/paper/2403.08462/) (2024)
- [InstructAV: Instruction Fine-tuning Large Language Models for Authorship Verification](https://mlsanity.com/paper/2407.12882/) (2024)
- [Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches](https://mlsanity.com/paper/2505.15422/) (2025)
- [IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation](https://mlsanity.com/paper/2604.04704/) (2026)
- [LG4AV: Combining Language Models and Graph Neural Networks for Author Verification](https://mlsanity.com/paper/2109.01479/) (2021)
- [From stage to page: language independent bootstrap measures of distinctiveness in fictional speech](https://mlsanity.com/paper/2301.05659/) (2023)
- [Authorship identification under domain shift: a survey of stylistic measures and learned author representations](https://mlsanity.com/paper/2310.00436/) (2023)
- [Cross-Genre Authorship Attribution via LLM-Based Retrieve-and-Rerank](https://mlsanity.com/paper/2510.16819/) (2025)
- [Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New Perspective](https://mlsanity.com/paper/2311.02237/) (2023)

## Abstract (arXiv)

Authorship verification (AV) is a research subject in the field of digital text forensics that concerns itself with the question, whether two documents have been written by the same person. During the past two decades, an increasing number of proposed AV approaches can be observed. However, a closer look at the respective studies reveals that the underlying characteristics of these methods are rarely addressed, which raises doubts regarding their applicability in real forensic settings. The objective of this paper is to fill this gap by proposing clear criteria and properties that aim to improve the characterization of existing and future AV approaches. Based on these properties, we conduct three experiments using 12 existing AV approaches, including the current state of the art. The examined methods were trained, optimized and evaluated on three self-compiled corpora, where each corpus focuses on a different aspect of applicability. Our results indicate that part of the methods are able to cope with very challenging verification cases such as 250 characters long informal chat conversations (72.7% accuracy) or cases in which two scientific documents were written at different times with an average difference of 15.6 years (> 75% accuracy). However, we also identified that all involved methods are prone to cross-topic verification cases.