# Position: AI/ML Influencers Have a Place in the Academic Process

arXiv: https://arxiv.org/abs/2401.13782 · HTML: https://mlsanity.com/paper/2401.13782/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond](https://mlsanity.com/paper/2109.00725/) (2021)
- [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://mlsanity.com/paper/2211.13308/) (2022)
- [Estimating the Causal Effect of Early ArXiving on Paper Acceptance](https://mlsanity.com/paper/2306.13891/) (2023)
- [The role of twitter in the life cycle of a scientific publication](https://mlsanity.com/paper/1305.0435/) (2013)

## Built on by (most cited first, 3 of 3)

- [Position: Stop Making Unscientific AGI Performance Claims](https://mlsanity.com/paper/2402.03962/) (2024)
- [Position: The Artificial Intelligence and Machine Learning Community Should Adopt a More Transparent and Regulated Peer Review Process](https://mlsanity.com/paper/2502.00874/) (2025)
- [AutoPR: Let's Automate Your Academic Promotion!](https://mlsanity.com/paper/2510.09558/) (2025)

## Abstract (arXiv)

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.