# Cracking Double-Blind Review: Authorship Attribution with Deep Learning

arXiv: https://arxiv.org/abs/2211.07467 · HTML: https://mlsanity.com/paper/2211.07467/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## 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)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [On the Use of ArXiv as a Dataset](https://mlsanity.com/paper/1905.00075/) (2019)
- [Establishing Baselines for Text Classification in Low-Resource Languages](https://mlsanity.com/paper/2005.02068/) (2020)
- [To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions](https://mlsanity.com/paper/2205.14700/) (2022)
- [A Decade of In-text Citation Analysis based on Natural Language Processing and Machine Learning Techniques: An overview of empirical studies](https://mlsanity.com/paper/2008.13020/) (2020)
- [Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding](https://mlsanity.com/paper/2009.14445/) (2020)
- [A Machine Learning Framework for Authorship Identification From Texts](https://mlsanity.com/paper/1912.10204/) (2019)

## Built on by (most cited first, 6 of 6)

- [Trends and Challenges in Authorship Analysis: A Review of ML, DL, and LLM Approaches](https://mlsanity.com/paper/2505.15422/) (2025)
- [Assessing Deanonymization Risks with Stylometry-Assisted LLM Agent](https://mlsanity.com/paper/2602.23079/) (2026)
- [Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling](https://mlsanity.com/paper/2406.08218/) (2024)
- [From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review](https://mlsanity.com/paper/2602.22438/) (2026)
- [Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation](https://mlsanity.com/paper/2603.11936/) (2026)
- [Large Language Models Threaten Double-blind Review](https://mlsanity.com/paper/2608.05157/) (2026)

## Abstract (arXiv)

Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced researchers can often correctly guess from which research group an anonymous submission originates, biasing the peer-review process. In this work, we present a transformer-based, neural-network architecture that only uses the text content and the author names in the bibliography to attribute an anonymous manuscript to an author. To train and evaluate our method, we created the largest authorship identification dataset to date. It leverages all research papers publicly available on arXiv amounting to over 2 million manuscripts. In arXiv-subsets with up to 2,000 different authors, our method achieves an unprecedented authorship attribution accuracy, where up to 73% of papers are attributed correctly. We present a scaling analysis to highlight the applicability of the proposed method to even larger datasets when sufficient compute capabilities are more widely available to the academic community. Furthermore, we analyze the attribution accuracy in settings where the goal is to identify all authors of an anonymous manuscript. Thanks to our method, we are not only able to predict the author of an anonymous work, but we also provide empirical evidence of the key aspects that make a paper attributable. We have open-sourced the necessary tools to reproduce our experiments.