# GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement

arXiv: https://arxiv.org/abs/2609.26361 · HTML: https://mlsanity.com/paper/2609.26361/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Quantifying Long-Term Scientific Impact](https://mlsanity.com/paper/1306.3293/) (2013)
- [The Pace of Artificial Intelligence Innovations: Speed, Talent, and Trial-and-Error](https://mlsanity.com/paper/2009.01812/) (2020)
- [S2AND: A Benchmark and Evaluation System for Author Name Disambiguation](https://mlsanity.com/paper/2103.07534/) (2021)
- [Predicting the long-term citation impact of recent publications](https://mlsanity.com/paper/1503.09156/) (2015)
- [Do altmetrics correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective](https://mlsanity.com/paper/1401.4321/) (2014)
- [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)
- [An Overview on Evaluating and Predicting Scholarly Article Impact](https://mlsanity.com/paper/2008.03867/) (2020)
- [TweetPap: A Dataset to Study the Social Media Discourse of Scientific Papers](https://mlsanity.com/paper/2106.07213/) (2021)

## Abstract (arXiv)

With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingly difficult. For researchers, quickly identifying impactful work is essential, yet manually reviewing each new publication is impractical. Automated impact prediction methods help address this challenge, usually by combining various information sources available, such as a paper's content or citation history. In this work, we propose using GitHub engagement as an additional source and demonstrate that it provides both a timely and accurate signal. To this end, we introduce GitScholar, a novel dataset that links GitHub activity from 444,000 repositories to over 558,000 AI arXiv papers. Our experiments show that GitHub reactions improve early prediction precision by up to 12% over a strong academic baseline. Additionally, we find that GitHub signal offers near-complete coverage of high-impact AI papers, and consistently correlates with future academic success. GitScholar is publicly available at https://huggingface.co/datasets/huawei-csl/GitScholar.