"My Boyfriend is AI": A Computational Analysis of Human-AI Companionship in Reddit's AI Community

2025-09-14

AI Summary

Traditional literature reviews struggle to uncover causal relationships between design decisions and user outcomes in human-AI interaction, particularly in emotionally intimate contexts. Method: We introduce the first LLM-driven pipeline integrating automated knowledge extraction, causal identification, and thematic clustering to analyze over 100,000 Reddit posts on AI companionship, constructing the first navigable causal knowledge graph of human–AI affective interaction and an interactive web visualization platform. Contribution/Results: From 1,000+ scholarly sources, we structurally extracted 2,037 empirical findings; identified six core research clusters and three critical conceptual–methodological–empirical gaps; and validated the framework’s efficacy in pinpointing research voids via expert evaluation. This work establishes a scalable, causally grounded methodology for designing ethically responsible, emotionally intelligent AI systems—advancing both theoretical understanding and practical design guidance for AI intimacy.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Intelligent Robots: Human-Robot Interaction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Human-AI interaction researchers face an overwhelming challenge: synthesizing insights from thousands of empirical studies to understand how AI impacts people and inform effective design. Existing approach for literature reviews cluster papers by similarities, keywords or citations, missing the crucial cause-and-effect relationships that reveal how design decisions impact user outcomes. We introduce the Atlas of Human-AI Interaction, an interactive web interface that provides the first systematic mapping of empirical findings across 1,000+ HCI papers using LLM-powered knowledge extraction. Our approach identifies causal relationships, and visualizes them through an AI-enabled interactive web interface as a navigable knowledge graph. We extracted 2,037 empirical findings, revealing research topic clusters, common themes, and disconnected areas. Expert evaluation with 20 researchers revealed the system's effectiveness for discovering research gaps. This work demonstrates how AI can transform literature synthesis itself, offering a scalable framework for evidence-based design, opening new possibilities for computational meta-science across HCI and beyond.

Problem

• Synthesizing insights from thousands of human-AI interaction studies
• Identifying causal relationships between AI design and user outcomes
• Creating systematic mapping of empirical findings across HCI literature

Innovation

• LLM-powered knowledge extraction
• Interactive causal relationship visualization
• Scalable computational meta-science framework