# Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution

arXiv: https://arxiv.org/abs/1609.06686 · HTML: https://mlsanity.com/paper/1609.06686/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2016.

## Built on by (most cited first, 21 of 21)

- [On the State of the Art in Authorship Attribution and Authorship Verification](https://mlsanity.com/paper/2209.06869/) (2022)
- [Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification](https://mlsanity.com/paper/2310.08123/) (2023)
- [Whodunit? Learning to Contrast for Authorship Attribution](https://mlsanity.com/paper/2209.11887/) (2022)
- [DeepStyle: User Style Embedding for Authorship Attribution of Short Texts](https://mlsanity.com/paper/2103.11798/) (2021)
- [Fingerprinting Fine-tuned Language Models in the Wild](https://mlsanity.com/paper/2106.01703/) (2021)
- [Avengers Ensemble! Improving Transferability of Authorship Obfuscation](https://mlsanity.com/paper/2109.07028/) (2021)
- [Syllabic Quantity Patterns as Rhythmic Features for Latin Authorship Attribution](https://mlsanity.com/paper/2110.14203/) (2021)
- [SYSML: StYlometry with Structure and Multitask Learning: Implications for Darknet Forum Migrant Analysis](https://mlsanity.com/paper/2104.00764/) (2021)
- [A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution](https://mlsanity.com/paper/2410.21716/) (2024)
- [AIDBench: A benchmark for evaluating the authorship identification capability of large language models](https://mlsanity.com/paper/2411.13226/) (2024)
- [A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation](https://mlsanity.com/paper/2203.11849/) (2022)
- [Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy](https://mlsanity.com/paper/2306.02488/) (2023)
- [Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT](https://mlsanity.com/paper/2403.05519/) (2024)
- [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)
- [Capturing Style in Author and Document Representation](https://mlsanity.com/paper/2407.13358/) (2024)
- [Enhancing Authorship Attribution through Embedding Fusion: A Novel Approach with Masked and Encoder-Decoder Language Models](https://mlsanity.com/paper/2411.00411/) (2024)
- [Protecting Anonymous Speech: A Generative Adversarial Network Methodology for Removing Stylistic Indicators in Text](https://mlsanity.com/paper/2110.09495/) (2021)
- [TraSE: Towards Tackling Authorial Style from a Cognitive Science Perspective](https://mlsanity.com/paper/2206.10706/) (2022)
- [URM4DMU: an user represention model for darknet markets users](https://mlsanity.com/paper/2303.10674/) (2023)
- [Per-sender neural network classifiers for email authorship validation](https://mlsanity.com/paper/2509.00005/) (2025)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision. Recently, character-level and multi-channel CNNs have exhibited excellent performance for sentence classification tasks. We apply CNNs to large-scale authorship attribution, which aims to determine an unknown text's author among many candidate authors, motivated by their ability to process character-level signals and to differentiate between a large number of classes, while making fast predictions in comparison to state-of-the-art approaches. We extensively evaluate CNN-based approaches that leverage word and character channels and compare them against state-of-the-art methods for a large range of author numbers, shedding new light on traditional approaches. We show that character-level CNNs outperform the state-of-the-art on four out of five datasets in different domains. Additionally, we present the first application of authorship attribution to reddit.