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

2026-09-22

AI Summary

为预测AI研究影响，本文提出利用GitHub活动数据作为新信息源，并构建了GitScholar数据集，实验表明该方法能提高预测准确性。

Abstract

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.

Problem

• AI research
• impact prediction
• GitHub engagement
• publications
• automation

Innovation

• GitHub Engagement
• Impact Prediction
• Dataset