# Authorship clustering using multi-headed recurrent neural networks

arXiv: https://arxiv.org/abs/1608.04485 · HTML: https://mlsanity.com/paper/1608.04485/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2016.

## Built on by (most cited first, 2 of 2)

- [Authorship Attribution in Bangla Literature (AABL) via Transfer Learning using ULMFiT](https://mlsanity.com/paper/2403.05519/) (2024)
- [Better Call Claude: Can LLMs Detect Changes of Writing Style?](https://mlsanity.com/paper/2508.00680/) (2025)

## Abstract (arXiv)

A recurrent neural network that has been trained to separately model the language of several documents by unknown authors is used to measure similarity between the documents. It is able to find clues of common authorship even when the documents are very short and about disparate topics. While it is easy to make statistically significant predictions regarding authorship, it is difficult to group documents into definite clusters with high accuracy.