mlsanityData to 14 Oct 2026

When Nobody Around Is Real: Exploring Public Opinions and User Experiences On the Multi-Agent AI Social Platform

arXiv 2601.18275 ↗First posted 26 Jan 2026cs.HC cs.CYcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate

Abstract (arXiv)

Powered by large language models, a new genre of multi-agent social platforms has emerged. Apps such as Social.AI deploy numerous AI agents that emulate human behavior, creating unprecedented bot-centric social networks. Yet, existing research has predominantly focused on one-on-one chatbots, leaving multi-agent AI platforms underexplored. To bridge this gap, we took Social.AI as a case study and performed a two-stage investigation: (i) content analysis of 883 user comments; (ii) a 7-day diary study with 20 participants to document their firsthand platform experiences. While public discourse expressed greater skepticism, the diary study found that users did project a range of social expectations onto the AI agents. While some user expectations were met, the AI-dominant social environment introduces distinct problems, such as attention overload and homogenized interaction. These tensions signal a future where AI functions not merely as a tool or an anthropomorphized actor, but as the dominant medium of sociality itself-a paradigm shift that foregrounds new forms of architected social life.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 17 of its 66 references that are in the map. First posted on arXiv 26 Jan 2026.

Builds on

17 of 66 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate30 May
  2. 2023Generative Agents: Interactive Simulacra of Human Behavior7 Apr

Other references · 15

  1. 2025Why Do Multi-Agent LLM Systems Fail?cited by 604
  2. 2014The Rise of Social Botscited by 96
  3. 2017The spread of low-credibility content by social botscited by 73
  4. 2024The Dark Side of AI Companionship: A Taxonomy of Harmful Algorithmic Behaviors in Human-AI Relationshipscited by 62
  5. 2019How should my chatbot interact? A survey on human-chatbot interaction designcited by 56
  6. 2023Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Mediacited by 30
  7. 2024AI Companions Reduce Lonelinesscited by 28
  8. 2023ChoiceMates: Supporting Unfamiliar Online Decision-Making with Multi-Agent Conversational Interactionscited by 22
  9. 2024Multi-Agents are Social Groups: Investigating Social Influence of Multiple Agents in Human-Agent Interactionscited by 16
  10. 2025Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Mediacited by 9
  11. 2025MIND: Towards Immersive Psychological Healing with Multi-agent Inner Dialoguecited by 6
  12. 2025DialogueAgents: A Hybrid Agent-Based Speech Synthesis Framework for Multi-Party Dialoguecited by 6
3 more
  1. 2025The Dual Personas of Social Media Botscited by 3
  2. 2024Exploring the Impact of Anthropomorphism in Role-Playing AI Chatbots on Media Dependency: A Case Study of Xuanhe AIcited by 2
  3. 2025Beyond Individual UX: Defining Group Experience(GX) as a New Paradigm for Group-centered AIcited by 2

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