# Authorship identification under domain shift: a survey of stylistic measures and learned author representations

arXiv: https://arxiv.org/abs/2310.00436 · HTML: https://mlsanity.com/paper/2310.00436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 241 references that are in the map.
- First posted on arXiv 30 Sep 2023.

## Builds on (most cited first, 30 of 45)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates](https://mlsanity.com/paper/1804.10959/) (2018)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [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)
- [Learning Interpretable Style Embeddings via Prompting LLMs](https://mlsanity.com/paper/2305.12696/) (2023)
- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [A Deep Metric Learning Approach to Account Linking](https://mlsanity.com/paper/2105.07263/) (2021)
- [Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution](https://mlsanity.com/paper/1609.06686/) (2016)
- [Learning Invariant Representations of Social Media Users](https://mlsanity.com/paper/1910.04979/) (2019)
- [The Importance of Suppressing Domain Style in Authorship Analysis](https://mlsanity.com/paper/2005.14714/) (2020)
- [Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles](https://mlsanity.com/paper/2109.03158/) (2021)
- [The Topic Confusion Task: A Novel Scenario for Authorship Attribution](https://mlsanity.com/paper/2104.08530/) (2021)
- [Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework](https://mlsanity.com/paper/2109.04817/) (2021)
- [Whodunit? Learning to Contrast for Authorship Attribution](https://mlsanity.com/paper/2209.11887/) (2022)
- [Can Authorship Representation Learning Capture Stylistic Features?](https://mlsanity.com/paper/2308.11490/) (2023)
- [Learning Stylometric Representations for Authorship Analysis](https://mlsanity.com/paper/1606.01219/) (2016)
- [Author Identification using Multi-headed Recurrent Neural Networks](https://mlsanity.com/paper/1506.04891/) (2015)
- [PART: Pre-trained Authorship Representation Transformer](https://mlsanity.com/paper/2209.15373/) (2022)
- [Siamese Networks for Large-Scale Author Identification](https://mlsanity.com/paper/1912.10616/) (2019)
- [Assessing the Applicability of Authorship Verification Methods](https://mlsanity.com/paper/1906.10551/) (2019)
- [Neural Authorship Attribution: Stylometric Analysis on Large Language Models](https://mlsanity.com/paper/2308.07305/) (2023)
- [Authorship Attribution through Function Word Adjacency Networks](https://mlsanity.com/paper/1406.4469/) (2014)
- [DeepStyle: User Style Embedding for Authorship Attribution of Short Texts](https://mlsanity.com/paper/2103.11798/) (2021)

## Built on by (most cited first, 1 of 1)

- [Tuning for TraceTarnish: Techniques, Trends, and Testing Tangible Traits](https://mlsanity.com/paper/2512.03465/) (2025)

## Abstract (arXiv)

Authorship identification uses patterns in writing to infer who wrote a text, but those patterns also reflect topic, genre, and register. This survey argues that topic-independence is not a property of a stylistic feature but of the feature together with its encoding, its scoring rule, and the evaluation split, so the question is which combinations preserve author differences when the domain changes. We organize the evidence in four layers, domains and evaluation conditions, linguistic measures, representations, and scoring and decision rules, and examine within-study comparisons from classical frequency measures to learned author representations and language-model-mediated writing. These comparisons show why feature inventories alone do not explain performance: normalization and encoding change results, author-supervised learning can retain content, and evaluation protocols can reverse model rankings. Successful identification, stylistic similarity, and generalization therefore require separate evidence. The literature offers a practical way to obtain it through designs that separate author identity from particular circumstances of writing: the same authors writing across genres, different authors writing within the same conversation, and parallel texts with controlled content. These designs also help evaluate content masking and language-model rewriting, whose effects depend on what identifying information remains. We use this evidence to guide the choice of stylistic representations and the comparisons needed to establish where they generalize.