# How many preprints have actually been printed and why: a case study of computer science preprints on arXiv

arXiv: https://arxiv.org/abs/2308.01899 · HTML: https://mlsanity.com/paper/2308.01899/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories](https://mlsanity.com/paper/1808.05053/) (2018)
- [Popularity of arXiv.org within Computer Science](https://mlsanity.com/paper/1710.05225/) (2017)
- [arXiv e-prints and the journal of record: An analysis of roles and relationships](https://mlsanity.com/paper/1306.3261/) (2013)
- [Citation Count Analysis for Papers with Preprints](https://mlsanity.com/paper/1805.05238/) (2018)
- [Accelerating Scientific Publication in Biology](https://mlsanity.com/paper/1509.03775/) (2015)
- [arXiv@25: Key findings of a user survey](https://mlsanity.com/paper/1607.08212/) (2016)
- [Can we use Google Scholar to identify highly-cited documents?](https://mlsanity.com/paper/1804.10439/) (2018)

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

- [Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis](https://mlsanity.com/paper/2403.16303/) (2024)
- [Beat: Bi-directional One-to-Many Embedding Alignment for Text-based Person Retrieval](https://mlsanity.com/paper/2406.05620/) (2024)
- [Governance of Generative Artificial Intelligence for Companies](https://mlsanity.com/paper/2403.08802/) (2024)
- [Large Language Models for History, Philosophy, and Sociology of Science: Interpretive Uses, Methodological Challenges, and Critical Perspectives](https://mlsanity.com/paper/2506.12242/) (2025)
- [Exploring the change in scientific readability following the release of ChatGPT](https://mlsanity.com/paper/2506.21825/) (2025)
- [A Tertiary Review of Large Language Model-Based Code Generating Tasks: Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2605.25536/) (2026)

## Abstract (arXiv)

Preprints play an increasingly critical role in academic communities. There are many reasons driving researchers to post their manuscripts to preprint servers before formal submission to journals or conferences, but the use of preprints has also sparked considerable controversy, especially surrounding the claim of priority. In this paper, a case study of computer science preprints submitted to arXiv from 2008 to 2017 is conducted to quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version. In view of this issue, we introduce a semantics-based mapping method with the employment of Bidirectional Encoder Representations from Transformers (BERT). With this new mapping method and a plurality of data sources, we find that 66% of all sampled preprints are published under unchanged titles and 11% are published under different titles and with other modifications. A further analysis was then performed to investigate why these preprints but not others were accepted for publication. Our comparison reveals that in the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code.