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

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. • Affiliation type, attributed to a paper, is a continuum between academia and industry. • Conditioned meme score measures idea spread given a type of affiliation. • Most highly cited papers are those co-authored with mixed affiliations. • In general, the contagiousness of single memes does not depend on affiliation. • Memes present in both affiliations spread more easily through Big Tech than Academia.

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Publication Details

Journal
Journal of Informetrics
Published
2024-08-23
DOI
https://doi.org/10.1016/j.joi.2024.101572
Citations
9
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
17.05

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article

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

Bartosz Pieliński, Stanisław Giziński, Emilia Wiśnios, Paulina Kaczyńska et al.
9 citations
Journal of Informetrics
Computational and Text Analysis Methods
17.05
article

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

Bartosz Pieliński, Stanisław Giziński, Emilia Wiśnios, Paulina Kaczyńska, Przemysław Biecek, Julian Sienkiewicz, Hubert Ruczyński
article en
9 citations

Abstract

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. • Affiliation type, attributed to a paper, is a continuum between academia and industry. • Conditioned meme score measures idea spread given a type of affiliation. • Most highly cited papers are those co-authored with mixed affiliations. • In general, the contagiousness of single memes does not depend on affiliation. • Memes present in both affiliations spread more easily through Big Tech than Academia.

Journal of InformetricsVol. 18(4)
Warsaw University of Technology (PL), NASK National Research Institute (PL), University of Warsaw (PL)
European Commission
Openalex Percentile: Top 1%
Computational and Text Analysis Methods
17.05
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