# Grammar as a Behavioral Biometric: Using Cognitively Motivated Grammar Models for Authorship Verification

arXiv: https://arxiv.org/abs/2403.08462 · HTML: https://mlsanity.com/paper/2403.08462/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 145 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Image-based Recommendations on Styles and Substitutes](https://mlsanity.com/paper/1506.04757/) (2015)
- [Same Author or Just Same Topic? Towards Content-Independent Style Representations](https://mlsanity.com/paper/2204.04907/) (2022)
- [Explainable Authorship Verification in Social Media via Attention-based Similarity Learning](https://mlsanity.com/paper/1910.08144/) (2019)
- [A Deep Metric Learning Approach to Account Linking](https://mlsanity.com/paper/2105.07263/) (2021)
- [The Importance of Suppressing Domain Style in Authorship Analysis](https://mlsanity.com/paper/2005.14714/) (2020)
- [Author Identification using Multi-headed Recurrent Neural Networks](https://mlsanity.com/paper/1506.04891/) (2015)
- [Assessing the Applicability of Authorship Verification Methods](https://mlsanity.com/paper/1906.10551/) (2019)
- [Similarity Learning for Authorship Verification in Social Media](https://mlsanity.com/paper/1908.07844/) (2019)
- [POSNoise: An Effective Countermeasure Against Topic Biases in Authorship Analysis](https://mlsanity.com/paper/2005.06605/) (2020)
- [Sockpuppet Detection in Wikipedia: A Corpus of Real-World Deceptive Writing for Linking Identities](https://mlsanity.com/paper/1310.6772/) (2013)
- [Detecting Ghostwriters in High Schools](https://mlsanity.com/paper/1906.01635/) (2019)

## Built on by (most cited first, 4 of 4)

- [Authorship Impersonation via LLM Prompting does not Evade Authorship Verification Methods](https://mlsanity.com/paper/2603.29454/) (2026)
- [Fusing Stylometric and Embedding Systems to Estimate Authorship Likelihood Ratios in Japanese](https://mlsanity.com/paper/2606.13991/) (2026)
- [Normalisation-Based Likelihood Ratio Estimation for Forensic Authorship Verification](https://mlsanity.com/paper/2607.09501/) (2026)
- [Authorship Verification of Transcribed German-Language Videos](https://mlsanity.com/paper/2607.29168/) (2026)

## Abstract (arXiv)

Authorship Verification (AV) is a key area of research in digital text forensics, which addresses the fundamental question of whether two texts were written by the same person. Numerous computational approaches have been proposed over the last two decades in an attempt to address this challenge. However, existing AV methods often suffer from high complexity, low explainability and especially from a lack of clear scientific justification. We propose a simpler method based on modeling the grammar of an author following Cognitive Linguistics principles. These models are used to calculate λG (LambdaG): the ratio of the likelihoods of a document given the candidate's grammar versus given a reference population's grammar. Our empirical evaluation, conducted on twelve datasets and compared against seven baseline methods, demonstrates that LambdaG achieves superior performance, including against several neural network-based AV methods. LambdaG is also robust to small variations in the composition of the reference population and provides interpretable visualizations, enhancing its explainability. We argue that its effectiveness is due to the method's compatibility with Cognitive Linguistics theories predicting that a person's grammar is a behavioral biometric.