Discerning Authorship in Online Health Communities: Experience, Trust, and Transparency Implications for Moderating AI

2026-04-21

AI Summary

This study addresses the challenge that users in online health communities struggle to distinguish between AI- and human-generated health advice, potentially undermining community trust. By leveraging large language models to generate health content and integrating online experiments, surveys, and qualitative analysis, the research systematically examines users’ ability to identify authorship across diverse health topics. Findings reveal that users generally lack accurate identification capabilities and often rely on unreliable cues for heuristic judgments. Moreover, the health topic significantly influences the consistency of these judgments. Building on these insights, the paper proposes a self-regulatory pathway for online communities that combines transparency mechanisms with context-sensitive trust-building strategies, offering a novel approach to governing AI-generated content in health-related digital spaces.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Application Domains: Humanities & Computational Social Science
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Web Mining and Content Analysis: Community question answering

Abstract

For online health communities, community trust is paramount. Yet, advances in Large Language Models (LLMs) generating advice may erode this trust, especially if users cannot identify whether LLMs have been used. We investigate the feasibility of community-based detection of health advice authorship and how self-moderation of LLMs could help enhance advice utilization. In an online experiment, we evaluate people's ability to distinguish AI-generated from human-written advice across two health conditions, considering lived experience with a condition, AI-recognition training, and user attitudes towards transparency and trust around AI use. Our results indicate the need for transparency coupled with trust. We find little evidence of people's ability to discern advice authorship. However, we find a consistent effect of the health condition. Our qualitative findings identify unreliable signals, resulting in flawed heuristic evaluations of the advice. Our findings point to opportunities to improve the self-moderation of LLM-based AI and aid community-based AI moderation.

Problem

• authorship detection
• online health communities
• AI transparency
• trust
• Large Language Models

Innovation

• authorship detection
• large language models
• online health communities
• AI transparency
• community moderation