Sources of Truth: A Multi-Platform, Multilingual Audit of Citations in AI Mental Health Information Queries

2026-08-31

AI Summary

研究通过审计三种AI平台对20个心理健康问题的引用来源，使用分类器分析15,942条引用，揭示了引用分布和平台偏好差异。

Abstract

Online health information seeking is shifting from keyword search, where users consider a ranked list of links, to conversational systems that compose a single answer and curate its citations. Source evaluation therefore passes from user to platform, yet what these systems surface is poorly characterized. We audited three free consumer products (ChatGPT, Perplexity, Google AI Overview) on twenty English mental health questions under two prompt conditions, with a subset of three also translated into six further languages of varying resource tiers. We recorded 15,942 citations across 1,140 responses and 1,713 unique domains, then classified every citation with a nine-category organizational typology applied by a deterministic classifier validated against human coding. Citations were heavily concentrated: the ten most-cited domains accounted for 43.6% of English citations, and government, commercial health, and academic sources were closely matched at roughly 22% each. Platforms differed little in typical citation volume but sharply in consistency and in the source types they favored. Explicitly requesting sources shifted composition only modestly. Non-English queries surfaced fewer citations and were routed to language-appropriate resources at significantly lower rates. We release the typology, classifier, and annotated corpus as reusable instruments for auditing generative health search.

Problem

• Online health information
• conversational systems
• source evaluation
• citations
• mental health

Innovation

• multi-platform audit
• multilingual citation analysis
• mental health information
• deterministic classifier
• source typology