mlsanityData to 7 Oct 2026

AI-Mediated Communication Can Steer Collective Opinion

arXiv 2605.16245 ↗First posted 15 May 2026cs.CY cs.AI cs.CLcited by 1

Not on the map. Why?

Abstract (arXiv)

Generative artificial intelligence (AI) is increasingly integrated into the online platforms where humans exchange opinions; large language models (LLMs) now polish users' posts on LinkedIn and provide context for content shared on X. While prior work has shown that AI can express biased opinions and shape individuals' opinions during human-AI interactions, less attention has been paid to its influence on collective opinion formation when mediating human-to-human communication. We address this gap via a combination of empirical and theoretical analyses. We show empirically that LLMs from multiple popular families introduce directional biases when instructed to edit human-written texts on contested topics, for example, nudging texts in favor of gun control and against atheism. Building on this observation, we introduce a mathematical model of opinion dynamics in which an AI system sits between users on a social network, transforming the opinions they express and perceive. By analytically characterizing the equilibrium of this model and performing simulations on real social network data, we show that biases introduced by AI in human-to-human communication can be amplified through the network and shift collective opinion in their direction. In light of these findings, we investigate whether such biases are controllable by online platforms. We audit the "Explain this post" feature on X and find evidence of pro-life bias in Grok's outputs on abortion-related content, which we trace back to specific design choices. We conclude with a discussion of the broader implications of our findings in relation to ongoing legislative efforts in the European Union.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 32 of its 79 references that are in the map. First posted on arXiv 15 May 2026.

Builds on

32 of 79 references.

References · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2023Whose Opinions Do Language Models Reflect?cited by 759
  4. 2022Fine-tuning language models to find agreement among humans with diverse preferencescited by 189
  5. 2023Generative artificial intelligence enhances creativity but reduces the diversity of novel contentcited by 165
  6. 2023Simulating Opinion Dynamics with Networks of LLM-based Agentscited by 153
  7. 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 145
  8. 2024Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Datacited by 143
  9. 2024On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trialcited by 105
  10. 2023On the Stability of Iterative Retraining of Generative Models on their own Datacited by 98
  11. 2024Model Collapse Demystified: The Case of Regressioncited by 80
  12. 2024Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voterscited by 71
20 more
  1. 2018Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networkscited by 58
  2. 2024Large Language Models Reflect the Ideology of their Creatorscited by 56
  3. 2024AI and the Problem of Knowledge Collapsecited by 34
  4. 2025Position: Model Collapse Does Not Mean What You Thinkcited by 32
  5. 2025Linear Representations of Political Perspective Emerge in Large Language Modelscited by 30
  6. 2024Empirical evidence of Large Language Model's influence on human spoken communicationcited by 27
  7. 2017Minimizing Polarization and Disagreement in Social Networkscited by 16
  8. 2017A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part Icited by 15
  9. 2024Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Biascited by 12
  10. 2025Language-Driven Opinion Dynamics in Agent-Based Simulations with LLMscited by 12
  11. 2016Opinion dynamics: models, extensions and external effectscited by 11
  12. 2024Braess's Paradox of Generative AIcited by 7
  13. 2023On the Relationship Between Relevance and Conflict in Online Social Link Recommendationscited by 6
  14. 2024Aligning Large Language Models with Diverse Political Viewpointscited by 6
  15. 2013Opinion Maximization in Social Networkscited by 5
  16. 2018Opinion Dynamics with Varying Susceptibility to Persuasioncited by 4
  17. 2020Adversarial Perturbations of Opinion Dynamics in Networkscited by 4
  18. 2015Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientistscited by 3
  19. 2023Adversaries with Limited Information in the Friedkin--Johnsen Modelcited by 1
  20. 2025Opinion Dynamics: A Comprehensive Overviewcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Roadmap to Impactful Pluralistic Alignment Researchcited by 0