As the number of accepted papers at AI and ML conferences reaches into the thousands, it has become unclear how researchers access and read research publications. In this paper, we investigate the role of social media influencers in enhancing the visibility of machine learning research, particularly the citation counts of papers they share. We have compiled a comprehensive dataset of over 8,000 papers, spanning tweets from December 2018 to October 2023, alongside controls precisely matched by 9 key covariates. Our statistical and causal inference analysis reveals a significant increase in citations for papers endorsed by these influencers, with median citation counts 2-3 times higher than those of the control group. Additionally, the study delves into the geographic, gender, and institutional diversity of highlighted authors. Given these findings, we advocate for a responsible approach to curation, encouraging influencers to uphold the journalistic standard that includes showcasing diverse research topics, authors, and institutions.

翻译：随着人工智能与机器学习会议接收论文数量达到数千篇，研究人员获取和阅读研究出版物的方式变得不再明晰。本文探究了社交媒体影响者在提升机器学习研究可见性方面的作用，尤其关注其分享论文的被引次数。我们构建了一个涵盖2018年12月至2023年10月推文数据的综合数据集，包含超过8,000篇论文，并精确匹配了9个关键协变量的对照组。统计与因果推断分析表明，经这些影响者推荐的论文引用次数显著增加，其中位被引次数是对照组的2至3倍。此外，本研究还深入分析了被突出展示作者的地区、性别及机构多样性。基于上述发现，我们倡导采取负责任的策展方式，鼓励影响者秉持新闻行业标准，展示多元化的研究主题、作者及机构。