Position: AI/ML Influencers Have a Place in the Academic Process
This addresses the problem of research accessibility for AI/ML researchers in an era of overwhelming publication volumes, though it is incremental in analyzing existing influencer impact.
The study investigated how social media influencers affect the visibility of machine learning research, finding that papers endorsed by influencers had median citation counts 2-3 times higher than a matched control group.
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.