Misinformation Exposure in the Chinese Web: A Cross-System Evaluation of Search Engines, LLMs, and AI Overviews

• URL: http://arxiv.org/abs/2602.22221v1
• Date: Mon, 15 Dec 2025 15:36:13 GMT
• Title: Misinformation Exposure in the Chinese Web: A Cross-System Evaluation of Search Engines, LLMs, and AI Overviews
• Authors: Geng Liu, Junjie Mu, Li Feng, Mengxiao Zhu, Francesco Pierri,
• Abstract summary: Large Language Models (LLMs) are increasingly integrated into search services, providing direct answers.<n>Yet their factual reliability in non-English web ecosystems remains poorly understood.<n>We introduce a fact-checking dataset of 12161 Chinese Yes/No questions derived from real-world online search logs.
• Score: 5.655762029601206
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly integrated into search services, providing direct answers that can reduce users' reliance on traditional result pages. Yet their factual reliability in non-English web ecosystems remains poorly understood, particularly when answering real user queries. We introduce a fact-checking dataset of 12~161 Chinese Yes/No questions derived from real-world online search logs and develop a unified evaluation pipeline to compare three information-access paradigms: traditional search engines, standalone LLMs, and AI-generated overview modules. Our analysis reveals substantial differences in factual accuracy and topic-level variability across systems. By combining this performance with real-world Baidu Index statistics, we further estimate potential exposure to incorrect factual information of Chinese users across regions. These findings highlight structural risks in AI-mediated search and underscore the need for more reliable and transparent information-access tools for the digital world.

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