How cyborg propaganda reshapes collective action

• URL: http://arxiv.org/abs/2602.13088v1
• Date: Fri, 13 Feb 2026 16:49:26 GMT
• Title: How cyborg propaganda reshapes collective action
• Authors: Jonas R. Kunst, Kinga Bierwiaczonek, Meeyoung Cha, Omid V. Ebrahimi, Marc Fawcett-Atkinson, Asbjørn Følstad, Anton Gollwitzer, Nils Köbis, Gary Marcus, Jon Roozenbeek, Daniel Thilo Schroeder, Jay J. Van Bavel, Sander van der Linden, Rory White, Live Leonhardsen Wilhelmsen,
• Abstract summary: A distinct threat to democracy is emerging via partisan coordination apps and artificial intelligence-what we term 'cyborg propaganda'<n>This architecture combines verified humans with adaptive algorithmic automation, enabling a closed-loop system.<n>We argue that cyborg propaganda fundamentally alters the digital public square, shifting political discourse from a democratic contest of individual ideas to a battle of algorithmic campaigns.
• Score: 7.802095759784913
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The distinction between genuine grassroots activism and automated influence operations is collapsing. While policy debates focus on bot farms, a distinct threat to democracy is emerging via partisan coordination apps and artificial intelligence-what we term 'cyborg propaganda.' This architecture combines large numbers of verified humans with adaptive algorithmic automation, enabling a closed-loop system. AI tools monitor online sentiment to optimize directives and generate personalized content for users to post online. Cyborg propaganda thereby exploits a critical legal shield: by relying on verified citizens to ratify and disseminate messages, these campaigns operate in a regulatory gray zone, evading liability frameworks designed for automated botnets. We explore the collective action paradox of this technology: does it democratize power by 'unionizing' influence (pooling the reach of dispersed citizens to overcome the algorithmic invisibility of isolated voices), or does it reduce citizens to 'cognitive proxies' of a central directive? We argue that cyborg propaganda fundamentally alters the digital public square, shifting political discourse from a democratic contest of individual ideas to a battle of algorithmic campaigns. We outline a research agenda to distinguish organic from coordinated information diffusion and propose governance frameworks to address the regulatory challenges of AI-assisted collective expression.

Related papers

• Generative Propaganda [36.28140423485487]
  Generative propaganda is the use of generative artificial intelligence to shape public opinion.<n>The term "deepfakes" exerts outsized discursive power in shaping defenders' expectations of misuse.<n>Deception was neither the main driver nor the main impact vector of AI's use.
  arXiv  Detail & Related papers  (2025-09-23T15:27:00Z)
• When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems [78.04679174291329]
  We introduce a proof-of-concept to simulate the risks of malicious multi-agent systems (MAS)<n>We apply this framework to two high-risk fields: misinformation spread and e-commerce fraud.<n>Our findings show that decentralized systems are more effective at carrying out malicious actions than centralized ones.
  arXiv  Detail & Related papers  (2025-07-19T15:17:30Z)
• How Malicious AI Swarms Can Threaten Democracy: The Fusion of Agentic AI and LLMs Marks a New Frontier in Information Warfare [40.42844888224356]
  Public opinion manipulation has entered a new phase, amplifying its roots in rhetoric and propaganda.<n>Advances in large language models (LLMs) and autonomous agents now let influence campaigns reach unprecedented scale and precision.<n>Researchers warn AI could foster mass manipulation.
  arXiv  Detail & Related papers  (2025-05-18T13:33:37Z)
• Generative AI as Digital Media [0.0]
  Generative AI is frequently portrayed as revolutionary or even apocalyptic.<n>This essay argues that such views are misguided.<n>Instead, generative AI should be understood as an evolutionary step in the broader algorithmic media landscape.
  arXiv  Detail & Related papers  (2025-03-09T08:58:17Z)
• Digital Democracy in the Age of Artificial Intelligence [0.16385815610837165]
  This chapter explores the influence of Artificial Intelligence (AI) on digital democracy.<n>It focuses on four main areas: citizenship, participation, representation, and the public sphere.
  arXiv  Detail & Related papers  (2024-11-26T10:20:53Z)
• Artificial Intelligence in Election Campaigns: Perceptions, Penalties, and Implications [44.99833362998488]
  We identify three categories of AI use -- campaign operations, voter outreach, and deception.<n>While people generally dislike AI in campaigns, they are especially critical of deceptive uses, which they perceive as norm violations.<n>Deception AI use increases public support for stricter AI regulation, including calls for an outright ban on AI development.
  arXiv  Detail & Related papers  (2024-08-08T12:58:20Z)
• LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control [72.81786007015471]
  We propose a learnable Perception-Action-Communication (LPAC) architecture for the problem.<n>CNN processes localized perception; a graph neural network (GNN) facilitates robot communications.<n> Evaluations show that the LPAC models outperform standard decentralized and centralized coverage control algorithms.
  arXiv  Detail & Related papers  (2024-01-10T00:08:00Z)
• A Scalable Multi-Robot Framework for Decentralized and Asynchronous Perception-Action-Communication Loops [72.1902268440158]
  Collaboration in large robot swarms to achieve a common global objective is a challenging problem in large environments.<n>The robots must execute a Perception-Action-Communication loop -- they perceive their local environment, communicate with other robots, and take actions in real time.<n>Recently, this has been addressed using Graph Neural Networks (GNNs) for applications such as flocking and coverage control.<n>This paper proposes a framework for asynchronous PAC in robot swarms, where decentralized GNNs are used to compute navigation actions and generate messages for communication.
  arXiv  Detail & Related papers  (2023-09-18T21:20:50Z)
• VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms [237.25930757584047]
  We propose to learn decentralized controllers based on solely raw visual inputs. For the first time, that integrates the learning of two key components: communication and visual perception. Our proposed learning framework combines a convolutional neural network (CNN) for each robot to extract messages from the visual inputs, and a graph neural network (GNN) over the entire swarm to transmit, receive and process these messages.
  arXiv  Detail & Related papers  (2020-02-06T15:25:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.