Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2312.12881

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

Stanisław Giziński·Paulina Kaczyńska·Hubert Ruczyński·Emilia Wiśnios·Bartosz Pieliński·Przemysław Biecek·Julian Sienkiewicz
View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

physics.soc-phcs.CLcs.SI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Stanisław Giziński, Paulina Kaczyńska, Hubert Ruczyński, Emilia Wiśnios, Bartosz Pieliński, Przemysław Biecek, Julian Sienkiewicz. 2024-08-24. Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliation. https://doi.org/10.1016/j.joi.2024.101572

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Allocation condensation under asymptotically complete mixing

We study a fixed network on which new mass is allocated in proportion to a power of each node's stock and then redistributed. Transport retains a fraction of both old and new mass at each node and divides the rest according to fixed background shares. We show that a small amount of retention can sustain concentration on networks where complete redistribution would leave every node's share of new mass vanishing as the network grows. A node receiving most new mass may retain little relative to the network total, yet much more than redistribution alone would supply. For a power above one, the allocation rule amplifies this relative stock advantage at the next step. We identify retention rates tending to zero with network size that allow widely spread and concentrated stationary allocations to coexist on the same network. One of them stays close to the allocation under complete redistribution. Each node can also dominate a separate concentrated allocation, receiving a share of new mass tending to one. The stocks supporting these allocations attract nearby trajectories, although every stationary stock approaches the same background, with even its largest share tending to zero. These allocations continue to coexist under specified changes in transport that preserve the background. Redistribution can therefore spread the stock widely without spreading new allocations in the same way.

physics.soc-ph↗
arXiv2026

Glassy dynamics of metropolitan human mobility frozen near the gravitational equilibrium

This research establishes a connection between macroscopic urban commuting flows and thermal equilibrium. Using mobility data from around 30 million records across six Japanese cities over one year, we introduce the Gravity-based Home Swapping Model (GHSM). This model applies Metropolis dynamics to urban commuting by treating individuals as interacting particles. Within this framework, total commuting time dictates the system energy, and temperature controls how strongly mobility responds to cost savings. Consequently, urban commuting can be analyzed through an evaluable free energy and simulated similarly to physical systems of matter. The maximumentropy doubly constrained gravity model serves as the stationary state. We find that the residential dynamics exhibit glassy characteristics. Through calibration to observed data, we reveal that the system demonstrates signatures of kinetically constrained models, specifically ageing, hysteresis, and freezing near equilibrium. Simulations initialized from arbitrary states converge to empirical origin-destination matrices. This demonstrates that minimal physical mechanisms can reconstruct complex urban realities.

physics.soc-ph↗
arXiv2026

Human-Agent Interaction in Synthetic Social Networks: A Framework for Studying Online Polarization

Online social networks have dramatically altered the landscape of public discourse, creating both opportunities for enhanced civic participation and risks of deepening social divisions. Prevalent approaches to studying online polarization have been limited by a methodological disconnect: mathematical models excel at formal analysis but lack linguistic realism, while language model-based simulations capture natural discourse but often sacrifice analytical precision. This paper introduces an innovative computational framework that synthesizes these approaches by embedding formal opinion dynamics principles within LLM-based artificial agents, enabling both rigorous mathematical analysis and naturalistic social interactions. We validate our framework through comprehensive offline testing and experimental evaluation with 122 human participants engaging in a controlled social network environment. The results demonstrate our ability to systematically investigate polarization mechanisms while preserving ecological validity. Our findings reveal how polarized environments shape user perceptions and behavior: participants exposed to polarized discussions showed markedly increased sensitivity to emotional content and group affiliations, while perceiving reduced uncertainty in the agents' positions. By combining mathematical precision with natural language capabilities, our framework opens new avenues for investigating social media phenomena through controlled experimentation. This methodological advancement allows researchers to bridge the gap between theoretical models and empirical observations, offering unprecedented opportunities to study the causal mechanisms underlying online opinion dynamics.

physics.soc-ph↗