# Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliation

arXiv: https://arxiv.org/abs/2312.12881 · HTML: https://mlsanity.com/paper/2312.12881/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 54 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research](https://mlsanity.com/paper/2010.15581/) (2020)
- [The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integrity](https://mlsanity.com/paper/2009.13676/) (2020)
- [A narrowing of AI research?](https://mlsanity.com/paper/2009.10385/) (2020)
- [The Privatization of AI Research(-ers): Causes and Potential Consequences -- From university-industry interaction to public research brain-drain?](https://mlsanity.com/paper/2102.01648/) (2021)
- [Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics](https://mlsanity.com/paper/1406.7091/) (2014)
- [Ethical Considerations and Statistical Analysis of Industry Involvement in Machine Learning Research](https://mlsanity.com/paper/2006.04541/) (2020)
- [Analyzing the Impact of Companies on AI Research Based on Publications](https://mlsanity.com/paper/2310.20444/) (2023)
- [Impact of lexical and sentiment factors on the popularity of scientific papers](https://mlsanity.com/paper/1605.07465/) (2016)
- [Inheritance patterns in citation networks reveal scientific memes](https://mlsanity.com/paper/1404.3757/) (2014)
- [Deep learning, deep change? Mapping the development of the Artificial Intelligence General Purpose Technology](https://mlsanity.com/paper/1808.06355/) (2018)
- [Citation Cascade and the Evolution of Topic Relevance](https://mlsanity.com/paper/2004.12275/) (2020)
- [Knowledge Evolution in Physics Research: An Analysis of Bibliographic Coupling Networks](https://mlsanity.com/paper/1704.00875/) (2017)

## Built on by (most cited first, 2 of 2)

- [Irresponsible AI: big tech's influence on AI research and associated impacts](https://mlsanity.com/paper/2512.03077/) (2025)
- [Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias](https://mlsanity.com/paper/2512.05714/) (2025)

## Abstract (arXiv)

There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or memes. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences. The main goal of this paper is to unravel the specific nuances of such influence, determining which AI ideas are predominantly driven by Big Tech entities. By employing network and memetic analysis on AI-oriented paper abstracts and their citation network, we are able to grasp a deeper insight into this phenomenon. By utilizing two databases: OpenAlex and S2ORC, we are able to perform such analysis on a much bigger scale than previous attempts. Our findings suggest that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia. Focusing on the most contagious memes, their attribution to specific affiliation groups (Big Tech, Academia, mixed affiliation) seems equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.