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

2026-01-26

AI Summary

This study addresses a critical gap in current research, which predominantly focuses on single-agent conversational systems while neglecting systematic investigations of user perception and interaction within multi-agent AI social platforms. Taking Social.AI as a case study, the authors combine content analysis of 883 user reviews with a seven-day diary study involving 20 participants to uncover novel phenomena in AI-mediated social environments—namely, users’ projection of social expectations onto AI agents, experiences of attentional overload, and interaction homogenization. The findings reveal that, despite prevailing skepticism, users actively assign social roles to AI entities. More significantly, the work proposes a paradigm shift in conceptualizing AI as a socio-relational medium, offering both empirical grounding and theoretical insights into the transformative potential and associated risks of multi-agent AI systems in reshaping social life.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: User Experience and Usability

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• multi-agent AI social platform
• user experience
• public opinion
• AI-driven sociality
• social interaction

Innovation

• multi-agent AI social platform
• large language models
• user experience
• architected sociality
• AI-mediated interaction