# Learning Stylometric Representations for Authorship Analysis

arXiv: https://arxiv.org/abs/1606.01219 · HTML: https://mlsanity.com/paper/1606.01219/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2016.

## Built on by (most cited first, 14 of 14)

- [Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification](https://mlsanity.com/paper/2310.08123/) (2023)
- [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)
- [Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators](https://mlsanity.com/paper/2203.09813/) (2022)
- [DeepStyle: User Style Embedding for Authorship Attribution of Short Texts](https://mlsanity.com/paper/2103.11798/) (2021)
- [An Overview of Recent Work in Media Forensics: Methods and Threats](https://mlsanity.com/paper/2204.12067/) (2022)
- [Can Authorship Attribution Models Distinguish Speakers in Speech Transcripts?](https://mlsanity.com/paper/2311.07564/) (2023)
- [Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution](https://mlsanity.com/paper/2110.14203/) (2021)
- [Integrating Bidirectional Long Short-Term Memory with Subword Embedding for Authorship Attribution](https://mlsanity.com/paper/2306.14933/) (2023)
- [Authorship identification under domain shift: a survey of stylistic measures and learned author representations](https://mlsanity.com/paper/2310.00436/) (2023)
- [I still know it's you! On Challenges in Anonymizing Source Code](https://mlsanity.com/paper/2208.12553/) (2022)
- [Estimating the Influence of Sequentially Correlated Literary Properties in Textual Classification: A Data-Centric Hypothesis-Testing Approach](https://mlsanity.com/paper/2411.04950/) (2024)
- [Stylometry recognizes human and LLM-generated texts in short samples](https://mlsanity.com/paper/2507.00838/) (2025)
- [Stability-Aware Feature Design for Robust Watermark Detection in Machine-Generated Text](https://mlsanity.com/paper/2608.18102/) (2026)

## Abstract (arXiv)

Authorship analysis (AA) is the study of unveiling the hidden properties of authors from a body of exponentially exploding textual data. It extracts an author's identity and sociolinguistic characteristics based on the reflected writing styles in the text. It is an essential process for various areas, such as cybercrime investigation, psycholinguistics, political socialization, etc. However, most of the previous techniques critically depend on the manual feature engineering process. Consequently, the choice of feature set has been shown to be scenario- or dataset-dependent. In this paper, to mimic the human sentence composition process using a neural network approach, we propose to incorporate different categories of linguistic features into distributed representation of words in order to learn simultaneously the writing style representations based on unlabeled texts for authorship analysis. In particular, the proposed models allow topical, lexical, syntactical, and character-level feature vectors of each document to be extracted as stylometrics. We evaluate the performance of our approach on the problems of authorship characterization and authorship verification with the Twitter, novel, and essay datasets. The experiments suggest that our proposed text representation outperforms the bag-of-lexical-n-grams, Latent Dirichlet Allocation, Latent Semantic Analysis, PVDM, PVDBOW, and word2vec representations.